Patent Application
20180268238
The present invention relates to the use of license plate recognition and image feature matching processes in automatic vehicle access control, parking management, automatic toll collection and security applications. More specifically, the invention relates to enhancements in license plate recognition and image feature matching processes for real-time applications.
The growing demand for personal and public safety, security of property, and efficient toll and parking payment collection mechanisms has prompted the development of intelligent traffic surveillance and monitoring systems. The first and foremost requirement for the success of automatic traffic monitoring and control systems is to achieve a high degree of accuracy in identifying vehicles from their license plates and other signatures. Autonomous traffic control systems require minimum human intervention and utilize automatic means for actuating gates and barriers to allow or deny vehicles to pass, and must meet stringent accuracy criteria. Efficient vehicle access control systems ensure fast and easy entry and exit in secure facilities, parking lots and toll stations for authorized vehicles, while preventing traffic congestion and unauthorized intrusions. Considering the traffic and security needs of an organization, managers can either go for manned or unmanned vehicle access control systems. Unmanned vehicle access control systems either come as hands-free type that automatically open gates and barriers for authorized vehicles, or require card readers, bio-metric scanners, key fob or cell-phones to operate the gates and barriers. Hands-free automatic vehicle access control (AVAC) systems are most attractive because of their hassle free operation.
AVAC systems can broadly be divided into two categories: Radio Frequency Identification (RFID) based systems and LPR based systems. RFID based systems are generally considered to be most secure by virtue of the error detection and correction information embedded in the RFID tags. However, use of RFID tags have their limitations. Drivers have to volunteer to get registered with the tag issuing authority and have to pay for the service. Vehicle owners have to attach RFID tags to the windscreens of their vehicles and take care that these are not disturbed or obstructed. Placing RFID tag may be particularly difficult if the windscreen has a metallic sun-protecting coating. These systems sometimes operate only at short ranges and are generally unable to pinpoint the exact location of a tag. Moreover, these systems may get confused if several tags are sensed in the vicinity. On the other hand, LPR based systems utilize video cameras to capture images of vehicles and use OCR algorithms to read license plate numbers to identify these vehicles. These systems can be deployed universally as all countries require vehicles to be equipped with at least one license plate. LPR systems can read license plates at longer ranges and do not place any maintenance burden on the end-user. Besides, LPR systems utilize day/night cameras and generate compelling evidence of traffic and other violations that is presentable in a court of law. Moreover, these systems can easily be used to target and flag vehicles wanted by law enforcement agencies.
Despite their advantages, OCR inaccuracies constitute a major hurdle in the success of LPR based systems, resulting in reading errors, and thus limiting their utility. Reading license plates becomes challenging due to a number of factors including poor quality or damaged license plates, improper lighting, multitude of fonts and plate types, fancy plate holders and weather or aging effects. Moreover, in LPR based recognition systems security may be compromised by fake license plates. It is for these reasons that LPR based vehicle recognition is mostly limited to applications where 80% to 90% reading accuracy is considered acceptable. AVAC systems on the other hand, demand much higher recognition rates. Therefore, to successfully deploy LPR based AVAC systems there is a need to improve their plate/vehicle recognition accuracy.
Realizing the above major deficiency in OCR based LPR systems, a number of innovations have been proposed in different inventions to improve the vehicle recognition accuracy. U.S. Pat. No. 7,466,223B2 and WO2008076463A1 disclose methods for automatic vehicle access control where in the event of LPR failure security personnel or parking supervisor are called for manual intervention, who may then correct the misread and allow the vehicle to enter. However, no effort is made on the part of the system to prevent the misread from occurring again. U.S. Pat. No. 8,781,172B2 describes methods to enhance general LPR systems by performing OCR on multiple captured images, combining the results, and obtaining the best plate number on the basis of maximum confidence level thresholds. These methods, however, cannot be applied to damaged or tampered license plates that have been rendered machine unreadable. Patent application No. US20130132166A1 discloses a toll network that improves vehicle identification to find matching pairs of vehicles at toll exit points. In this disclosure, first LPR based identification is performed, next, signature based identification is performed for unpaired vehicles, next, supplemental processing is performed to compare partial matches, and finally, human inspection is performed by narrowing the choices presented to the inspector. The disclosed methods, however, are not applicable to AVAC systems as signature matching and pairing of vehicles is performed only at exit points. Moreover, the system excludes all the vehicles that have exited the toll station from further processing. Thus, it does not improve its performance by taking advantage of the data of vehicles that routinely pass the toll station and form a major source of toll income. In addition, vehicle pairing by human inspection at exit points is a laborious and error prone process. U.S. Pat. No. 6,747,687B1 discloses methods for an entry-exit system that leave out the OCR altogether, and relies solely on image matching. Although generic, the disclosed methods can only be used for a limited number of cars as acknowledged by the inventors. The reason being that the most concise and unique feature of a vehicle, that is, the license plate number, has been ignored. Patent application No. US20110116686A1, Patent application No. US20110042462A1 and U.S. Pat. No. 9,025,828B2 describe methods to verify and correct OCR results where the license plate hosts additional graphic insignia such as a bar code or a sticker. These methods are not viable as they require replacing the existing license plates with new designs or mounting bar-codes on cars. Patent application No. US20050084134A1 tries to reduce OCR errors in an entry-exit system by receiving input from three sources (voice, keyboard, image) and synthesizing a plate number by giving highest priority to voice, then to the keyboard and finally to OCR. Such a system can only operate when the gates are continuously monitored. Also, no effort is made on the part of the system to prevent the OCR misreads from occurring again. U.S. Pat. No. 9,405,988B2 discloses an LPR system for roadway toll applications that improves plate reading accuracy by utilizing past verified data, and combining OCR and vehicle signature recognition technologies. The methods disclosed in this patent rely heavily on grouping images of the same vehicles along with extensive manual verification of images and text data. Problem with this method of grouping is that it depends on the number of times a vehicle is seen by the system and not on the difficulty level of plate reads. A vehicle with perfectly readable license plate that travels a road frequently will unnecessarily form a large image group by having all its captured instances stored by the system, even though OCR based plate read results alone could easily recognize it. Thus, precious system resources are wasted. A judicious utilization of system resources and a more efficient method of image grouping can be visualized where the system stores more images/features of vehicles that have difficult-to-read license plates to help identify such vehicles accurately, while storing less images/features and relying on OCR for easy-to-read license plates. In addition to the above difficulty, the manual image and text verification processes as disclosed by the above patent are cumbersome and error prone, requiring experienced reviewers along with a system to continuously monitor the performance of reviewers. Ideally, the manual feedback/verification process should be simple and more manageable. US patent application US20160092473A1 presents a method for parking management that captures and compares license plate features to identify vehicles at entry and exit points. However, the disclosed method ignores the most concise and unique feature of a vehicle, that is, the license plate number, while identifying vehicles. Moreover, it does not contain error handling in the case of image mismatches. Also, there is no provision of improving the performance of the system on the basis of past data. U.S. Pat. No. 8,265,988B2 describes a toll management and vehicle identification system where a first OCR stage is used to narrow down matching vehicle candidates. A second vehicle fingerprint identification stage operates on the candidates to determine the best matching pair. If a matching pair with reasonable confidence is not found a human operator is involved to manually identify a matching pair. Here it is worth noting that the number of candidates generated by the first stage can be large if the general quality of plates is poor. When this occurs, the complex fingerprint identification stage would become a bottleneck that would slow down traffic, causing congestion at toll exits. Moreover, the manual identification process described is cumbersome and does not apply to AVAC systems as fingerprint matching and pairing of vehicles is performed at toll exit points.
It is apparent that methods proposed in the prior art for LPR and feature recognition systems ignore computational efficiency and excessive memory usage aspects of the algorithms. These aspects are vital for successful deployment on low cost embedded platforms in a real-time scenario. Moreover, the role of human operator for error correction as described in the prior art is cumbersome and needs to be simplified. In particular, burdening the operator or the end user to visually compare vehicle matches or correct the reading errors of OCR should be avoided in AVAC applications. Another ignored aspect of LPR based systems pertains to the fact that 10% to 15% plate records inserted into LPR databases generally have reading errors. These errors are bound to adversely affect any future database query. An easy method of correcting misreads in plate records stored in an LPR database is required. Yet another important aspect that is not considered in the prior art is that conventional LPR systems tend to ignore vehicles whose plates were not readable, or vehicles where license plates were not found. This serious omission can prove costly as these very vehicles may be the ones that are wanted by law enforcement agencies. Thus, methods are needed to enable LPR systems to capture and categorize all vehicle records, as vehicles with read license plates, vehicles with unread license plates and vehicles without license plates, and store these categories in their databases for future reference. Moreover, LPR systems should not just record and store vehicle and plate images but also record short video clips of each passing vehicle as part of the plate record.
Signature matching of license plates and vehicles is achieved by comparing high dimensional feature vectors representing image patches around salient points (called corner points) in the images. Depending upon their types (floating point or binary) the feature vectors are compared by computing Euclidean or Hamming distance metrics. Euclidean distance in high dimensional space is hard to compute. Although fast approximate methods based on k-dimensional (k-d) trees have been proposed in the literature to reduce the complexity of computing Euclidean distance in high dimensional feature space, this operation still becomes a bottleneck when hundreds or thousands of license plate and vehicles images each represented by hundreds or thousands of high dimensional feature vectors are to be matched in real-time. On the other hand, binary feature vectors are compared using the Hamming distance metric, which for binary data can be computed by performing a bit-wise exclusive-OR (XOR) operation followed by a bit count on the result. This involves only bit manipulation operations which can be performed quickly, especially on modern computers where there is hardware support for counting the number of bits that are set in a word. Even though computing the distance between pairs of binary features can be done efficiently, using linear search for matching can be practical only for smaller data sets. For large data sets, linear matching becomes a bottleneck in most applications. Algorithms like k-d trees are not applicable for speeding up binary features comparison. Other algorithms such as those based on multiple hierarchical clustering trees are also not suitable for real-time applications including vehicle or license plate recognition, as the reference list of images is continuously being updated with the arrival of new vehicles. Hence, there is a need for methods that can speed-up the matching process of floating point or binary feature vectors in real-time signature matching applications. One objective of the present invention is to disclose simple and fast feature matching methods that are applicable to both floating point as well as binary feature vectors.
An embodiment of the present invention captures images and generates image corner points and their corresponding features, where an image may represent a license plate, a vehicle, or a part of a vehicle. The features may be expressed as floating point vectors, fixed point vectors or binary vectors that are sorted by their significance values (with the most significant feature at the top) and stored in a list. A preferred embodiment of the present invention utilizes prior art technique of Brown et al. Proc. CVPR-2005, pages 510-517, to compute the sorted list of features where significance values are expressed in terms of corner strengths and decreasing suppression radii. The disclosed method of the current invention selects C most significant corner points from the sorted list of each image, where C is the number of features that are deemed sufficient to reliably recognize an image from a group of reference images. The disclosed method stores C sorted features of each reference image in a database or any other data structure. Next, instead of performing a full linear search by matching C features of an image with C features of each reference image to find the best match, the disclosed method only matches the top F sorted features of an image with the top F sorted features of each reference image, and finds M closest (approximate) matching images. In a preferred embodiment, F is set much smaller than C, hence, finding M closest approximate matches requires order of magnitude less computations than the full linear search. Finally, the target image is compared with M best approximate matched reference images of the previous step by matching the entire list of C features of each image, to get an overall best match. It should be mentioned that the success of the above method lies in the fact that the image features used for comparison are sorted in an order of significance. Hence, the probability that the closest match found by the disclosed method is indeed the best match is extremely high. Moreover, even though the preferred embodiment of the invention uses the technique of Brown et al. 2005, to get an initial sorted list of features, any other feature sorting technique may be employed such as the technique described in U.S. Pat. No. 8,797,414B2.
In another embodiment of the present invention, further speedup in feature matching is achieved by extracting a summary of each feature, where the summary may constitute a sub-sampled version of a floating-point or binary feature. Thus, each feature vector is reordered into two parts: 1. a summary vector S; 2. the feature vector V (that is left behind after removing the summary). The two parts may be stored as separate entities or stored as a concatenation of the two. The combined feature vector F is formed by the union of S and V. The dimension of S is much smaller than that of V, and S may be considered a rough approximation of F. In an embodiment of the present invention, the process of computing the Euclidean distance for floating point feature vectors or Hamming distance for binary feature vectors, respectively, is as follows: First the summary vectors S are matched and the summary distance is computed. Next, if the summary distance found is higher than a predetermined threshold T the process is halted and the match is declared a bad match. On the other hand, if the summary distance is equal to or below the threshold T, the rest of the feature V is also matched and the combined distance of the feature F is computed by adding the two distances. By setting the threshold T to a suitable safe value, most of the bad matches are rejected at the summary matching stage, and only features that are close to each other are matched in detail. Thus computational complexity is reduced significantly.
Storage requirement of license plate and vehicle recognition systems based upon signature matching is typically high making implementation of prior art methods on embedded platforms highly challenging. Storing signatures of hundreds or thousands of images where each image is represented by a large number of high dimensional feature vectors requires excessive random access memory (RAM) and permanent storage space. A second objective of the present invention is to disclose methods that minimize storage requirement of license plate and vehicle recognition systems.
In a preferred embodiment of the present invention, storage requirements are minimized by replacing reference image features that have lost their utility, and avoiding storing multiple copies of reference images where ever possible. To keep storage requirements in check, the method discloses suitable conditions for adding new feature vectors of a license plate/vehicle to the reference feature data store, and replacing old feature vectors of a license plate/vehicle in the reference feature data store by the corresponding new feature vectors. In one embodiment of the present invention, new feature data corresponding to a license plate or a vehicle are added to the reference data when the image features show large differences when compared with the previously stored version(s) of the same image. In this way, different variants of feature data of a license plate or a vehicle are made available for future comparisons. The above method improves system accuracy when license plate or vehicle images are being captured under large variations in lighting conditions or when capture distances are varying. The method also helps when the cameras being used at different points have different characteristics. On the other hand, in another embodiment of the present invention, new feature data corresponding to a license plate or a vehicle replaces the previous feature data when the image features show small variations when compared with the previously stored version(s) of the same image. In this way any small changes that occur in the license plate or vehicle images over time are updated, and more representative and current features are available for improved future comparisons. It should be noted here that the difference between the current and previous feature data is computed through Euclidean or Hamming distance measures.
Another embodiment of the present invention associates the image feature addition and replacement policy with the OCR result accuracy or OCR result variation. The merit behind this strategy is that OCR accuracy/variation is a good indicator of image variability. If the difference in the images is small the OCR results remain stable and accurate, while if the difference in images is large the OCR results may vary and show inaccuracies. According to the disclosed method, if the OCR results for a certain license plate/vehicle are inaccurate or are varying, its new (current) image features are added to the reference features. On the other hand, if the OCR results for a certain license plate/vehicle are accurate or stable, new image features replace the old image features.
Prior art includes LPR systems that allow users to manually correct misread plate records that exist in their database, thereby improving the accuracy of subsequent database search queries. However, this manual correction is a time consuming and error prone exercise, where typically all capture instances of a misread plate are extracted by querying the database and manually corrected one by one. A third objective of the present invention is to simplify the process of locating and correcting misread errors in a large LPR database of plate records.
The invention discloses an LPR system where the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony (in the case where the captured license plate matches with a number in a hot license plate list of a law enforcement agency), and any other comments. The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle. A database correction technique is disclosed in the present invention whereby one misread plate is extracted from the database and manually corrected, while the system automatically searches and corrects all other instances of the same plate within the database with the help of pattern/feature matching of plate and/or vehicle images. The system may correct all the instances of the plate/vehicle images found in the database or may search and present all the instances to a user for manual verification and correction. In another embodiment of the invention, a license plate gets captured and is corrected manually by a user on-the-fly before storing the number in the LPR database. The LPR system stores the plate record with the corrected number and automatically searches the database for other instances of the same plate with the help of pattern/feature matching and corrects all other instances of the license plates. In another embodiment of the invention the system makes use of partial plate number matching to limit the number of candidates that are to be considered for automatic number correction.
Prior art includes LPR and vehicle signature recognition algorithms operating as part of AVAC or parking management systems that (in the case of plate misreads) allow operators and registered users to override system decisions and open barriers/gates through external means including card readers, bio-metric scanners, key fob, cell-phones, wireless transceiver, electro-mechanical switches/buttons, or PC/Web based applications operating in wired or wireless modes. In the case of misreads, prior art methods burden an operator/user to visually verify image matches and manually correct the misread plate by entering the correct plate number using a keypad, keyboard or voice input. A fourth objective of the present invention is to simplify the interaction of a user/operator with the system.
According to the disclosed method, when an unmanned AVAC system fails to match the number plate of an authorized vehicle and wrongly bars its entry, the vehicle's driver/passenger issues an overriding command that opens the gate, allowing the vehicle to pass/enter. The overriding command may be issued through external means such as card reader, bio-metric scanner, key fob, cell-phone/smart phone application, transceiver, electro-mechanical switch/button, PC/Web based application, or any other interface using wired or wireless means. In one embodiment of the invention, the overriding command may contain means to open the gate/barrier. In another embodiment of the present invention, the overriding command may contain means to open the gate/barrier and may contain embedded information regarding the identity of the vehicle including its plate number. Thus, the user is not burdened to provide this information explicitly. The overriding action of opening the gate/barrier through the said external means is used as a signal to the AVAC system to identify a difficult-to-read license plate. Likewise, the overriding command containing embedded information regarding the vehicle's identity is used to further tune the LPR and vehicle signature recognition processes to correct their errors. As a result, the system figures out that a misread has occurred, identifies OCR errors and categorizes it as a difficult-to-identify vehicle/license plate. The system then takes corrective actions to improve the recognition of the said vehicle without the user having to visually match vehicles or enter the correct plate number via keypad, keyboard or voice input. Thus, the task of the user/operator is simplified and the LPR/vehicle recognition system learns from experience and performs better when it encounters the same vehicle again.
Prior art includes LPR systems that read license plates of vehicles and store the license plate images, vehicle images, and license plate data as plate records in their database. These plate records can then be searched by querying the database. However, the conventional LPR systems do not keep track of plates that they were unable to read or of vehicles where they could not find any license plates. A fifth objective of the present invention is to disclose an LPR system that accounts for all the passing traffic irrespective of whether a license plate was read or not, or whether a license plate was not found on a vehicle.
To handle applications that demand all the passing traffic to be accounted for, the present invention discloses an LPR system that categorizes and stores license plate records as read license plates, unread license plates and vehicles without license plates. Here, read license plates pertain to vehicles whose plates were read by the system, unread license plates pertain to vehicles where the system found a mounted license plate but was unable to read it, and vehicles without license plates pertain to vehicles where the system could not find a mounted license plate. Hence, the system enables a user to not only search the read license plates but also the unread plates and even vehicles with license plates missing. Moreover, as an additional aid to users, the LPR system may also include a short video clip of the vehicle as part of the plate record. The video clip may be recorded via a color or infrared camera.
In some situations, OCR based license plate recognition and maintaining license plate records in databases is discouraged due to privacy concerns. An embodiment of the present invention discloses a privacy mode selection method that allows an AVAC system, a parking management or a traffic management system to select between a normal operating mode and a privacy (respecting) mode. In the normal operating mode, both OCR and image feature recognition processes are used for license plate and/or vehicle recognition. While in the privacy mode of operation, the system does not display or store any license plate number in human readable form. In privacy mode the system may also be instructed not to perform OCR on license plate images and to solely rely on image feature recognition and machine readable features. In one embodiment of the invention, in privacy mode, the OCR comes into play only when a violation occurs or an un-authorized vehicle is detected. Thus, privacy of authorized vehicles or non-violators is respected as license plate numbers of these vehicles are never converted into human readable form.
To handle diverse security needs, an embodiment of the present invention discloses a security level selection method that allows an AVAC system, a parking management system or a traffic management system to switch among a plurality of security levels (or modes). At higher security levels, identification errors are prevented by more stringent checks and verifications. In one embodiment of the present invention, the system relies on OCR/license plate image based feature recognition at the normal security level, includes car image feature recognition at the next higher level, and biometric features/face recognition/car under-carriage recognition at the highest security levels.
It is understood that other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein various embodiments of the invention are shown and described by way of illustration. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the spirit and scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.
The accompanying Figures, which are incorporated herein and form part of the specification, illustrate the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the relevant art(s) to make and use the invention.
The features and advantages of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. The drawing in which an element first appears is indicated by the leftmost digit(s) in the corresponding reference number.
The disclosure set forth below in connection with the appended drawings is intended as a description of various embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. In some instances, well-known structures and components are shown in block diagrams in order to avoid obscuring the concepts of the present invention. One or more embodiments of the present invention will now be described.
Furthermore, different embodiments of the software application 300 may contain features like dynamic video source detection, whereby the selection of the appropriate camera input source is made automatically on the basis of the availability of the video signal; and dynamic video standard detection, whereby the selection of NTSC/PAL/SECAM or HD video standards is made automatically. Different embodiments of the software application 300 may also provide support for FAT32, FAT16, HFS, HFS+, Ext2, Ext3, NTFS, or any other standard or proprietary file system for storing license plate records, images and video files. Moreover, an embodiment of the present invention may contain a TCP/IP stack or any other suitable communication stack to allow for connection to one or more networked devices. In addition to this, a preferred embodiment of the present invention uses at least one of a plurality of database formats including SQLite, SQL, MySQL or any other database format to store license plate records, images and videos.
In one embodiment of the present invention, the system reads a vehicle's license plate using OCR and matches the read number with its list of known (authorized) numbers 800. Two cases can arise as a result of this matching 801:
Case 1: the read license plate number matches with a reference license plate number. In this case the system matches and compares features of the read license plate/vehicle with those of the matched license plate number/vehicle 802. If a close match is found 804, the system replaces the old features of the license plate/vehicle in the reference feature store 405 with the new features 806. On the other hand, if a close match is not found 804, the new features are added in the reference feature store 405 for the said vehicle number. Thus a plurality of feature sets exist in the reference feature store for the read license plate number.
Case 2: the read license plate number does not match with a reference license plate number. For unread (difficult) plate cases the system maintains a difficult-to-identify reference image list. The system matches and compares features of the unmatched license plate/vehicle with those in the difficult-to-identify reference image list 803. If a close match is found 805 the system replaces the old features of the license plate/vehicle in the reference feature store 405 with the new features. On the other hand, if a close match is not found 805, the system receives user's overriding command or operator input to identify the vehicle 809. If the vehicle is identified as authorized 811 the new features are added in the reference feature store 405 for the said vehicle 812. On the other hand, if the vehicle is not identified as authorized 811, it is ignored 810.
When a vehicle arrives in the field of view of the LPR camera the LPR system captures at least one image of the vehicle 1200. The system then tries to find a license plate in the vehicle image 1201. If a license plate is not found 1202, the system stores the record in the not-found plate category in its database 1204. On the other hand, if a license plate is found 1202, the system employs an OCR to read the plate number 1203. The license plate may be damaged or dirty and the OCR algorithm may not be able to read it 1205. In this case the system stores the record in the unread plate category 1207. On the other hand, if the OCR algorithm is successful in reading the plate (even with errors) the system stores the record in the read plate category 1206. Hence, by storing the above plate record categories in the database, the system enables a user to not only search the read license plates but also the unread plates and even vehicles without license plates. Moreover, as an additional aid to the users, the LPR system may also include a short video clip of the vehicle as part of the plate record. The video clip may be recorded via a color or infrared camera.
To alleviate privacy concerns, an embodiment of the present invention discloses a privacy mode selection method that allows an AVAC system, a parking management or a traffic management system to select between a normal operating mode and a privacy mode. A simplified exemplary privacy mode selection process is shown in
To handle diverse security needs, an embodiment of the present invention discloses a security level selection method that allows an AVAC system, a parking management system or a traffic management system to switch among a plurality of security levels (or modes). A simplified exemplary security level selection process is shown in
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit of scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the full scope consistent with the claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more”. All structural and functional equivalents to the elements of the various embodiments described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 1 12, sixth paragraph, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for”.
