Obstacle tracking apparatus and method

Abstract

An obstacle tracking apparatus includes an image input unit which acquires image sequences; an obstacle detector which detects candidate areas of an obstacle at a current time from the image sequences; a state hypothesis storage which stores a state hypothesis group including at least one state hypothesis of the obstacle at a previous time; an measurement hypothesis generator which generates a measurement hypothesis group including at least one measurement hypothesis obtained by combining measurement hypotheses for the respective positions of candidate areas of the obstacle and a measurement hypothesis in case the obstacle is not detected; a likelihood calculator which calculates likelihoods of respective combinations of the respective state hypotheses included in the state hypothesis group and the respective measurement hypotheses included in the measurement hypothesis group; a state hypothesis updater which obtains a highest likelihood from the likelihoods of the respective combinations and updates the state hypotheses at the previous time stored in the state hypothesis storage using the state hypothesis group at the current time as the state hypothesis group having the highest likelihood; and a hypothesis selector which selects the state hypothesis having the highest likelihood from the state hypothesis group at the current time as a state in which the obstacle is detected.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart which also serves as a block diagram of an obstacle tracking apparatus according to an embodiment of the invention;

FIG. 2 is an explanatory drawing of a coordinate system in this embodiment;

FIG. 3 is an explanatory drawing of a case in which a plurality of measured positions exist; and

FIG. 4 is an explanatory drawing of a procedure for selecting a hypothesis.

Claims

1. An obstacle tracking apparatus comprising: an image acquiring unit mounted to a moving object and configured to acquire image sequences including an obstacle;an obstacle detecting unit configured to detect candidate areas of the obstacle at a current time from the image sequences;a state hypothesis storing unit configured to store a state hypothesis group including one or a plurality of state hypothesis or hypotheses of the obstacle at a previous time, the each state hypothesis relating to a motion of the obstacle;a measurement hypothesis generating unit configured to generate a measurement hypothesis group including one or a plurality of the measurement hypothesis or hypotheses obtained by combining measurement hypotheses for the respective positions of the candidate areas of the obstacle and a measurement hypothesis in case the obstacle is not detected;a likelihood calculating unit configured to calculate likelihoods of respective combinations of the respective state hypotheses included in the state hypothesis group and the respective measurement hypotheses included in the measurement hypothesis group;a state hypothesis updating unit configured to obtain a highest likelihood from the likelihoods of the respective combinations of the respective state hypotheses included in the state hypothesis group and the respective measurement hypotheses included in the measurement hypothesis group and update the state hypotheses at the previous time stored in the state hypothesis storing unit using the state hypothesis group at the current time as the state hypothesis group having the highest likelihood; anda hypothesis selecting unit configured to select the state hypothesis having the highest likelihood from the state hypothesis group at the current time as a state in which the obstacle is detected.

2. The obstacle tracking apparatus according to claim 1, wherein the state hypothesis is represented by kinetic information including the position of the obstacle and the likelihood.

3. The obstacle tracking apparatus according to claim 1, wherein the measurement hypothesis is represented by Gaussian distribution for each detected position of the obstacle.

4. The obstacle tracking apparatus according to claim 1, wherein the measurement hypothesis that the obstacle is not detected is represented by a uniform distribution.

5. The obstacle tracking apparatus according to claim 1, wherein the likelihood for each combination of the state hypothesis included in the state hypothesis group and the measurement hypothesis included in the measurement hypothesis group is calculated using a Kalman filter.

6. The obstacle tracking apparatus according to claim 1, wherein the kinetic information of the obstacle is obtained from the selected state hypothesis.

7. The obstacle tracking apparatus according to claim 6, wherein the reliability of the kinetic information is evaluated.

8. An obstacle tracking method comprising: acquiring image sequences including an obstacle;detecting candidate areas of obstacles at the current time from the image sequences;storing a state hypothesis group including one or a plurality of state hypothesis or hypotheses of the obstacle at a previous time, the each state hypothesis relating to a motion of the obstacle;generating a measurement hypothesis group including one or a plurality of the measurement hypothesis or hypotheses obtained by combining measurement hypotheses for the respective positions of candidate areas of the obstacle and a measurement hypothesis in case the obstacle is not detected;calculating likelihoods of respective combinations of the respective state hypotheses included in the state hypothesis group and the respective measurement hypotheses included in the measurement hypothesis group;obtaining highest likelihood from the likelihoods of the respective combinations of the respective state hypotheses included in the state hypothesis group and the respective measurement hypotheses included in the measurement hypothesis group and updating the stored state hypothesis at the previous time using the state hypothesis group at the current time as the state hypothesis group having the highest likelihood; andselecting the state hypothesis having the highest likelihood from the state hypothesis group at the current time as a state in which the obstacle is detected.

9. The obstacle tracking method according to claim 8, wherein the state hypothesis is represented by kinetic information including the position of the obstacle and the likelihood.

10. The obstacle tracking method according to claim 8, wherein the measurement hypothesis is represented by Gaussian distribution for each detected position of the obstacle.

11. The obstacle tracking method according to claim 8, wherein the measurement hypothesis that the obstacle is not detected is represented by a uniform distribution.

12. The obstacle tracking method according to claim 8, wherein the likelihood for each combination of the state hypothesis included in the state hypothesis group and the measurement hypothesis included in the measurement hypothesis group is calculated using a Kalman filter.

13. The obstacle tracking method according to claim 8, wherein the kinetic information of the obstacle is obtained from the selected state hypothesis.

14. The obstacle tracking method according to claim 13, wherein the reliability of the kinetic information is evaluated.

15. An obstacle tracking program for realizing: an image acquiring function mounted to a moving object for acquiring timage sequences including an obstacle;an obstacle detectin g function for detecting candidate areas of the obstacle at the current time from the image sequence;a state hypothesis storing function for storing a state hypothesis group including one or a plurality of state hypothesis or hypotheses of the obstacle at a previous time, the each state hypothesis relating to a motion of the obstacle;a measurement hypothesis generating function for generating a measurement hypothesis group including one or a plurality of the measurement hypothesis or hypotheses by combining measurement hypotheses for the respective positions of the candidate areas of the obstacle and a measurement hypothesis in case the obstacle is not detected;a likelihood calculating function for calculating likelihoods of respective combinations of the respective state hypotheses included in the state hypothesis group and the respective measurement hypotheses included in the measurement hypothesis group;a state hypothesis updating function for obtaining a highest likelihood from the likelihoods of the respective combinations of the respective state hypotheses included in the state hypothesis group and the respective measurement hypotheses included in the measurement hypothesis group and updating the state hypothesis at the previous time stored by the state hypothesis storing function using the state hypothesis group at the current time as the state hypothesis group having the highest likelihood; anda hypothesis selecting function for selecting the state hypothesis having the highest likelihood from the state hypothesis group at the current time as a state in which the obstacle is detected with a computer.

16. The obstacle tracking program according to claim 15, wherein the state hypothesis is represented by kinetic information including the position of the obstacle and the likelihood.

17. The obstacle tracking program according to claim 15, wherein the measurement hypothesis is represented by Gaussian distribution for each detected position of the obstacle.

18. The obstacle tracking program according to claim 15, wherein the measurement hypothesis that the obstacle is not detected is represented by a uniform distribution.

19. The obstacle tracking program according to claim 15, wherein the likelihood for each combination of the state hypothesis included in the state hypothesis group and the measurement hypothesis included in the measurement hypothesis group is calculated using a Kalman filter.

20. The obstacle tracking program according to claim 15, wherein the kinetic information of the obstacle is obtained from the selected state hypothesis.

21. The obstacle tracking program according to claim 20, wherein the reliability of the kinetic information is evaluated.