1. Field of the Disclosure
The present disclosure relates to a video object detection method of a camera, and more particularly to a method for adjusting parameters of a video object detection algorithm of a camera.
2. Description of the Related Art
Image security monitoring has a very wide range of applications around our living environment. When thousands of cameras installed at all corners in a city send recorded videos to a master control room, the management and the identification of back-end images become arduous. Therefore, in order to realize the purpose of security protection, screen monitoring is performed by personnel. Another effective resolution is to utilize the function of smart video object detection of cameras. However, the stability of the function of smart video object detection is very important, and directly correlates to whether or not consumers are willing to accept smart cameras.
One of the factors influencing the stability of smart video object detection is the change of on-site environmental conditions, which include weather changes, movement of an object, changes in the reflection angle of an object, and various other elements. When a photosensitive device in a camera receives light and transmits an image to a back-end screen for display, due to partial or integral changes of light rays of a scene recorded by the camera, an error rate of the function of smart video object detection for image analysis is increased, and the stability and the practicability of the function of smart video object detection are reduced.
Many researches had been carried out to address the problem of the light ray changes, but most of the research is focused on development of an algorithm model for counteracting the light ray changes, and desired results are successfully produced only in some ideal cases. Moreover, some researches set focus on building models for solving specific weather situations, for example, for rainy days, a foreground detection model that is not affected by rain is proposed. However, there are many challenges in the development of a new algorithm to address the light changes. For example, when a new model needs to be developed, the original algorithm needs to be abandoned. In addition, the original hardware or embedded system needs to be re-designed, and the additional development cannot be based on the original infrastructure. Furthermore, the results of the prior research may require more computational complexity than is permitted by the old model, so that the practicability is reduced on real-time detection.
Accordingly, a method for adjusting parameters of a video object detection algorithm of a camera and an apparatus using the same are needed. The method can be built on the original algorithm platform without using excessive research time to develop additional algorithms, thereby avoiding the difficulties faced in prior research.
The present disclosure is directed to a method for adjusting parameters of a video object detection algorithm of a camera and an apparatus using the same, which can adjust algorithm parameters according to environmental factors. According to the method and the apparatus of the present disclosure, optimization processing can be performed to improve accuracy of a smart video object detection function in different scenarios without any additional information provided by a user, so as to minimize the interference of environmental factors on the algorithm. Accordingly, after a long period of operation, the algorithm can maintain stable performance.
The present disclosure provides a method for adjusting parameters of a video object detection algorithm of a camera. The method includes the following steps: receiving a stream of training image signals, and dividing each frame of the training image signals into a plurality of regions; determining quantified values of environmental variables of the regions of each frame of the training image signals; performing video object detection on the stream of training image signals according to a video object detection algorithm to generate a stream of video object detection results; changing parameters of the video object detection algorithm and repeating the step of the video object detection to generate a plurality of streams of video object detection results; and comparing the video object detection results with a reference result to determine an optimum correspondence between the quantified values of the environmental variables and the parameters of the video object detection algorithm.
The present disclosure provides an apparatus for a video object detection algorithm of a camera. The apparatus includes a video object detection training module and a video object detection application module. The video object detection training module is configured to generate an optimum correspondence between quantified values of the environmental variables and parameters of a video object detection algorithm according to a stream of training video signals and a video object detection reference result. The video object detection application module is configured to perform video object detection on a stream of image signals based on the optimum correspondence between quantified values of the environmental variables and parameters of the video object detection algorithm.
The technical features of the present disclosure have been briefly described above so as to make the detailed description that follows more comprehensible. Other technical features that form the subject matters of the claims of the present disclosure are described below. It should be understood by persons of ordinary skill in the art of the present disclosure that the same objective as that of the present disclosure can be achieved by easily making modifications or designing other structures or processes based on the concepts and specific embodiments described below. It should also be understood by persons of ordinary skill in the art of the present disclosure that such equivalent constructions do not depart from the spirit and scope of the present disclosure defined by the appended claims.
The disclosure will be described according to the appended drawings in which:
The present disclosure provides a method for adjusting parameters of a video object detection algorithm of a camera and an apparatus using the same. In order to make the present disclosure more comprehensible, detailed steps and compositions are proposed in the following description. The implementation of the present disclosure is not limited to the specific details well known to persons of ordinary skill in the art. Furthermore, the well-known compositions or steps are not described in detail, so as to avoid unnecessary limitations on the present disclosure. Preferred embodiments of the present disclosure will be described in detail below, but in addition to the detailed description, the present disclosure can also be implemented in other embodiments, and the scope of the present disclosure is not limited thereto, and is defined by the following claims.
Preferably, the video object detection training module 120 includes a parameter training module 122 and a comparison module 124. The parameter training module 122 is configured to generate a plurality of streams of video object detection results according to the stream of training image signals and different parameter values. The comparison module 124 is configured to compare the stream video object detection results and the video object detection reference result, so as to generate an optimum correspondence between quantified values of the environmental variables and parameters of the video object detection algorithm. Preferably, the comparison module 124 compares the streams of video object detection results and the video object detection reference result to select an optimum video object detection result, and the comparison module 124 determines an optimum correspondence between quantified values of the environmental variables and parameters of the video object detection algorithm according to the optimum video object detection result. The video object detection application module 130 includes a parameter adjustment module 132. The parameter adjustment module 132 is configured to perform video object detection on the stream of image signals according to the optimum correspondence between the quantified values of the environmental variables and the parameters of the video object detection algorithm, so as to generate a stream of video object detection results.
Embodiments of performing video object detection by applying the apparatus in
The calculation process of the method in
S
n
=A(P∩T)/A(T)
S
p
=A(N∩F)/A(f−T),
where A(a) represents an area of a region a, f represents a set of pixels of the entire image, T is a set of object pixels in the video detection reference result, P is a set of object pixels of the video object detection result, F=f−T, and N=f−P. The accuracy of the video object detection result increases as Sn and Sp approach a value of 1. In other words, it is indicated that the parameter p used for resolving P is better. The comparison between the video object detection result and the video object detection reference result SC can be obtained through the formula below:
sc=(Sn+Sp)SnSp/2
After all the possible combinations of the parameters have been tested, a complete fraction sequence {S1, SC2, . . . , SCn
Hereinafter, a region ri is discussed. As the quantity of the data in the matrix is large, a quantified value S may correspond to a plurality of optimum parameters {p1s, p2s, . . . , pns}. Accordingly, in this embodiment, an average of the optimum parameters is taken as the most suitable parameter corresponding to the quantified value S of the environmental factor. If the standard deviation of the parameters is excessively large, for example, larger than a critical value, the parameters are not stable and can be abandoned. In this case, the optimum parameter corresponding to the quantified value S can be obtained via an interpolation method of other quantified values and the optimum parameters corresponding thereto.
After the above operation, a simplified two-dimensional data matrix M1 is obtained, and the dimension is nk×2, in which the quantified value S of each environmental variable only corresponds to one parameter pg, and the two-dimensional data matrix M1 is expressed as follows:
M
1
=[P
i
S
i],
where,
In order to further save the space in the storage device 140 for storing the optimum correspondence M1 and reduce the noise of the data, the two-dimensional data matrix can be described by a polynomial function:
where m is an integer determined according to the conditions. The polynomial function may be expressed in the form of a matrix:
F=
where F and A represent vectors having dimensions of nk and m, respectively, and
A=
+
F.
The vector A in the original formula is substituted accordingly, and the polynomial function can be expressed as:
F=S
+
P.
In Step 301, as in Step 201, each frame of the image signals is divided into nr regions. In Step 302, quantified values of environmental variables of the regions of each frame of the image signals are determined using the environmental variable calculation module 110. In Step 303, according to the optimum correspondence between the quantified values of environmental variables and the parameters of the video object detection algorithm, that is, the polynomial function F in
In view of the above, the present disclosure provides a method for adjusting parameters of a video object detection algorithm of a camera and an apparatus using the same, which can adjust the algorithm parameters according to the environmental factors. According to the method and the apparatus of the present disclosure, optimization processing can be performed for the accuracy of a smart video object detection function in different scenarios without additional information provided by a user, so as to minimize the extent of interference of the environmental factors on the algorithm. Accordingly, after long-term operation, the algorithm can maintain the most stable performance.
Although the technical contents and features of the present disclosure are described above, various replacements and modifications can be made by persons skilled in the art based on the teachings and disclosure of the present disclosure without departing from the spirit thereof. Therefore, the scope of the present disclosure is not limited to the described embodiments, but covers various replacements and modifications that do not depart from the present disclosure as defined by the appended claims.
Number | Date | Country | Kind |
---|---|---|---|
099141372 | Nov 2010 | TW | national |