Mobile body track identification system

• US 8,989,438 B2
• Filed: 08/13/2010
• Issued: 03/24/2015
• Est. Priority Date: 08/20/2009
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Moriguchi, Yukie, Konishi, Yuusuke, Nino, Yuichi
• Primary Examiner(s): ANSARI, TAHMINA N
• Application Number:
  US13/391,199
• Publication Number:
  US 20120148102A1
• Time in Patent Office: 1,684 Days
• Field of Search: None
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30241 Trajectory
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters

Abstract

There is provided a mobile body track identification system that determines which mobile body matches which detected track with a high precision irrespective of frequent interruption of tracks of a mobile body detected in a tracking area. Herein, hypotheses are generated by use of sets of track-coupling candidate/identification pairs, which combines track-coupling candidates, combining tracks of a mobile body detected in a predetermined time in the past, and identifications of the mobile body and which satisfies a predetermined condition. Next, identification likelihoods are calculated as likelihoods of detecting identifications in connection with tracks indicated by track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses. Identification likelihoods are integrated per each track-coupling candidate/identification pair, thus calculating an identification likelihood regarding the selected hypothesis. A most-probable hypothesis is estimated based on identification likelihoods of hypotheses.

Claims

1. A mobile body track identification system comprising:

a track-coupling candidate generation part that is configured to generate track-coupling candidates corresponding to combinations of tracks of mobile bodies detected in a predetermined time in the past;

a hypothesis generation part that is configured to generate sets of track-coupling candidate/identification pairs combining track-coupling candidates and identifications of mobile bodies detected in the predetermined time in the past, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying a predetermined condition; and

a likelihood calculation part which is configured to select individual hypotheses so as to calculate identification likelihoods representing likelihoods of detecting identifications with respect to tracks indicated by track-coupling candidates included in each of track-coupling candidate/identification pairs ascribed to each of the selected hypotheses, and to integrate identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood for each of the selected hypotheses, to estimate a most-probable hypothesis based on identification likelihoods of hypotheses.

2. The mobile body track identification system according to claim 1 further comprising a map storage that is configured to store a probability map defining detection probabilities of identifications of mobile bodies in response to positional coordinates of mobile bodies in the tracking area in advance,

wherein the likelihood calculation part includes

an identification likelihood calculation part which is configured to specify a position of a mobile body on tracks indicated by each track-coupling candidate at each identification detection time for each track-coupling candidate/identification pair ascribed to each of the selected hypotheses, to read a probability value of detecting the mobile body at the position from the probability map, to calculate an identification likelihood based on the probability value, to integrate identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood regarding each of the selected hypotheses, and

a most-probable estimation part that is configured to estimate the most-probable hypothesis based on identification likelihoods of hypotheses.

3. The mobile body track identification system according to claim 2 further comprising a likelihood storage that is configured to store track/identification correlation likelihoods representing likelihoods of correlating identifications to tracks of mobile bodies,

wherein the identification likelihood calculation part is configured to calculate a track/identification correlation likelihood, corresponding to a current combination of a track and an identification included in track-coupling candidates in track-coupling candidate/identification pairs ascribed to the most-probable hypothesis and all the hypotheses generated by the hypothesis generation part, so as to store them in the likelihood storage, and

wherein the identification likelihood calculation part is further configured to calculate an identification likelihood regarding each track-coupling candidate/identification pair based on the track/identification correlation likelihood corresponding to the current combination of the track and the identification included in track-coupling candidates for each track-coupling candidate/identification pair as well as the probability value which is read from the probability map at each identification detection time.

4. The mobile body track identification system according to claim 2 further comprising a hypothesis storage that is configured to store the most-probable hypothesis and all the hypotheses generated by the hypothesis generation part, and an information history variation determination part that is configured to determine whether or not variations occur in identifications detected in the predetermined time in the past,

wherein the track-coupling candidate generation part is configured to generate track-coupling candidates when the information history variation determination part determines that variations occur in identifications,

wherein the hypothesis generation part is configured to generate hypotheses when the information history variation determination part determines that variations occur in identifications,

wherein individual hypotheses are to be selected from among hypotheses generated by the hypothesis generation part when the information history variation determination part determines that variations occur in identifications, while individual hypotheses are to be selected from among hypotheses stored in the hypothesis storage when the information history variation determination part determines that no variations occur in identifications, and

wherein the most-probable hypothesis is to be estimated among hypotheses generated by the hypothesis generation part when the information history variation determination part determines that variations occur in identifications, whereby the track-coupling candidate generation part is further to store the most-probable hypothesis in the hypothesis storage together with all the hypotheses generated by the hypothesis generation part.

5. The mobile body track identification system according to claim 2, wherein the hypothesis generation part is further configured to correlate attributes of mobile bodies detected in the predetermined time in the past to track-coupling candidates so as to generate sets of track-coupling candidate/identification pairs, combining track-coupling candidates, correlated to attributes, and identifications of mobile bodies, to generate hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying a predetermined condition,

wherein the likelihood calculation part includes an attribute likelihood calculation part which is configured to calculate an attribute likelihood representing a likelihood of indicating the same mobile body with attributes, correlated to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses, and which to integrate attribute likelihoods for each track-coupling candidate/identification pair, thus calculating an attribute likelihood regarding each of the selected hypotheses, and

wherein the most-probable estimation part is further configured to estimate the most-probable hypothesis based on identification likelihoods and attribute likelihoods of hypotheses.

6. The mobile body track identification system according to claim 2, wherein the hypothesis generation part is further configured to correlate mobility values of mobile bodies detected in the predetermined time in the past to track-coupling candidates so as to generate sets of track-coupling candidate/identification pairs combining track-coupling candidates correlated to mobility values and identifications of mobile bodies, to generate hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying the predetermined condition,

wherein the likelihood calculation part includes a mobility likelihood calculation part which is configured to calculate a mobility likelihood representing a likelihood of indicating the same mobile body with mobility values correlated to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses, and which integrates mobility likelihoods for each track-coupling candidate/identification pair, to calculate a mobility likelihood for each of the selected hypotheses, and

wherein the most-probable estimation part is further configured to estimate the most-probable hypothesis based on identification likelihoods and mobility likelihoods of hypotheses.

7. The mobile body track identification system according to claim 2, wherein the likelihood calculation part further includes a track-coupling likelihood calculation part that is configured to calculate a track-coupling likelihood representing a likelihood of connecting tracks included in each of track-coupling candidates in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses, and

wherein the most-probable estimation part is further configured to estimate the most-probable hypothesis based on identification likelihoods and track-coupling likelihoods of hypotheses.

8. The mobile body track identification system according to claim 7, wherein the track-coupling likelihood calculation part is further configured to calculate track-coupling likelihoods based on detection times and positional coordinates of mobile bodies defining tracks.

9. The mobile body track identification system according to claim 1, wherein the track-coupling candidate generation part is further configured to calculate track-coupling likelihoods representing likelihoods of connecting tracks included in the generated track-coupling candidates so as to select track-coupling candidates whose track-coupling likelihoods are equal to or above a threshold, and

wherein the hypothesis generation part is further configured to generate hypotheses by use of the selected track-coupling candidates.

10. The mobile body track identification system according to claim 1 further comprising:

an environmental information storage that is configured to store environmental information by use of a position of an obstruction disposed in a tracking area and a transient time of a mobile body passing by the obstruction,

wherein the hypothesis generation part is further configured to calculate a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate, and to estimate a transient time of the mobile body from a track disappearance time to a next track appearance time based on the environmental information, to preclude track-coupling candidates with the estimated transient time longer than the real transient time from track-coupling candidates for use in generation of hypotheses.

11. The mobile body track identification system according to claim 1 further comprising an environmental information storage that is configured to store environmental information representing a transient time of a mobile body passing along a passable path in a tracking area,

wherein the hypothesis generation part is configured to calculate a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate, and to estimate a transient time of a mobile body from a track disappearance time to a next track appearance time based on the environmental information, to preclude track coupling candidates each having the estimated transient time longer than the real transient time from track-coupling candidates for use in generation of hypotheses.

12. A mobile body track identification method comprising:

generating track-coupling candidates corresponding to combinations of tracks of mobile bodies detected in a predetermined time in the past;

generating sets of track-coupling candidate/identification pairs combining track-coupling candidates and identifications of mobile bodies detected in the predetermined time in the past, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying a predetermined condition;

selecting individual hypotheses so as to calculate identification likelihoods representing likelihoods of detecting identifications with respect to tracks indicated by track-coupling candidates included in each of track-coupling candidate/identification pairs ascribed to each of the selected hypotheses;

integrating identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood for each of the selected hypotheses; and

estimating a most-probable hypothesis based on identification likelihoods of hypotheses.

13. The mobile body track identification method according to claim 12 further comprising:

storing a probability map defining detection probabilities of identifications of mobile bodies in response to positional coordinates of mobile bodies in the tracking area in advance;

specifying a position of a mobile body on tracks indicated by each track-coupling candidate at each identification detection time for each track-coupling candidate/identification pair ascribed to each of the selected hypotheses;

reading a probability value of detecting the mobile body at the position from the probability map;

calculating an identification likelihood based on the probability value;

further integrating identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood regarding each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods of hypotheses.

14. The mobile body track identification method according to claim 13 further comprising:

storing track/identification correlation likelihoods representing likelihoods of correlating identifications to tracks of mobile bodies;

calculating a track/identification correlation likelihood, corresponding to a current combination of a track and an identification included in track-coupling candidates in track-coupling candidate/identification pairs ascribed to the most-probable hypothesis and all other hypotheses; and

calculating an identification likelihood regarding each track-coupling candidate/identification pair based on the track/identification correlation likelihood corresponding to the current combination of the track and the identification included in track-coupling candidates for each track-coupling candidate/identification pair as well as the probability value read from the probability map at each identification detection time.

15. The mobile body track identification method according to claim 13 further comprising:

storing the most-probable hypothesis and relevant hypotheses;

determining whether or not variations occur in identifications detected in the predetermined time in the past;

generating track-coupling candidates so as to generate hypotheses when it is determined that variations occur in identifications;

selecting individual hypotheses from among the generated hypotheses when it is determined that variations occur in identifications, while selecting individual hypotheses from among the stored hypotheses when it is determined that no variations occur in identifications; and

upon estimating the most-probable hypothesis among the generated hypotheses when it is determined that variations occur in identifications, storing the most-probable hypothesis together with all the other generated hypotheses.

16. The mobile body track identification method according to claim 13 further comprising:

correlating attributes of mobile bodies detected in the predetermined time in the past to track-coupling candidates so as to generate sets of track-coupling candidate/identification pairs, combining track-coupling candidates, correlated to attributes, and identifications of mobile bodies, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying a predetermined condition;

calculating an attribute likelihood representing a likelihood of indicating the same mobile body with attributes, correlated to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses;

integrating attribute likelihoods for each track-coupling candidate/identification pair, thus calculating an attribute likelihood regarding each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods and attribute likelihoods of hypotheses.

17. The mobile body track identification method according to claim 13

further comprising:

correlating mobility values of mobile bodies detected in the predetermined time in the past to track-coupling candidates so as to generate sets of track-coupling candidate/identification pairs combining track-coupling candidates correlated to mobility values and identifications of mobile bodies, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying the predetermined condition;

calculating a mobility likelihood representing a likelihood of indicating the same mobile body with mobility values correlated to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses;

integrating mobility likelihoods for each track-coupling candidate/identification pair, thus calculating a mobility likelihood for each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods and mobility likelihoods of hypotheses.

18. The mobile body track identification method according to claim 13 further comprising:

calculating a track-coupling likelihood representing a likelihood of connecting tracks included in each of track-coupling candidates in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods and track-coupling likelihoods of hypotheses.

19. The mobile body track identification method according to claim 18 further comprising:

calculating track-coupling likelihoods based on detection times and positional coordinates of mobile bodies used to define tracks.

20. The mobile body track identification method according to claim 12 further comprising:

calculating track-coupling likelihoods representing likelihoods of connecting tracks included in track-coupling candidates so as to select track-coupling candidates whose track-coupling likelihoods are equal to or above a threshold; and

generating hypotheses by use of the selected track-coupling candidates.

21. The mobile body track identification method according to claim 12 further comprising:

storing environmental information by use of a position of obstruction disposed in a tracking area and a transient time of a mobile body passing by the obstruction;

calculating a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate;

estimating a transient time of the mobile body from a track disappearance time to a next track appearance time based on the environmental information; and

precluding track-coupling candidates each having the estimated transient time longer than the real transient time from track-coupling candidates for use in generation of hypotheses.

22. The mobile body track identification method according to claim 12 further comprising:

storing environmental information representing a transient time of a mobile body along a passable path in a tracking area;

calculating a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate;

estimating a transient time of a mobile body from a track disappearance time to a next track appearance time based on the environmental information; and

precluding track-coupling candidates each having the estimated transient time longer than the real transient time from track-coupling candidates for use in generation of hypotheses.

23. A non-transitory computer-readable storage medium implementing a mobile body track identification method comprising:

generating track-coupling candidates corresponding to combinations of tracks of mobile bodies detected in a predetermined time in the past;

generating sets of track-coupling candidate/identification pairs combining track-coupling candidates and identifications of mobile bodies detected in the predetermined time in the past, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying a predetermined condition;

selecting individual hypotheses so as to calculate identification likelihoods representing likelihoods of detecting identifications with respect to tracks indicated by track-coupling candidates included in each of track-coupling candidate/identification pairs ascribed to each of the selected hypotheses;

integrating identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood for each of the selected hypotheses; and

estimating a most-probable hypothesis based on identification likelihoods of hypotheses.

24. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 23 further comprising:

storing a probability map defining detection probabilities of identifications of mobile bodies in response to their positional coordinates in the tracking area in advance;

specifying a position of a mobile body on tracks indicated by each track-coupling candidate at each identification detection time for each track-coupling candidate/identification pair ascribed to each of the selected hypotheses;

reading a probability value of detecting the mobile body at the position from the probability map;

calculating an identification likelihood based on the probability value;

further integrating identification likelihoods for each track-coupling candidate/identification pair so as to calculate an identification likelihood regarding each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods of hypotheses.

25. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 24 further comprising:

storing track/identification correlation likelihoods representing likelihoods of correlating identifications to tracks of mobile bodies;

calculating a track/identification correlation likelihood, corresponding to a current combination of a track and an identification included in track-coupling candidates in track-coupling candidate/identification pairs ascribed to the most-probable hypothesis and all other hypotheses; and

calculating an identification likelihood regarding each track-coupling candidate/identification pair based on the track/identification correlation likelihood corresponding to the current combination of the track and the identification included in track-coupling candidates for each track-coupling candidate/identification pair as well as the probability value read from the probability map at each identification detection time.

26. The non-statutory computer-readable storage medium implementing a mobile body track identification method according to claim 24 further comprising:

storing the most-probable hypothesis and relevant hypotheses;

determining whether or not variations occur in identifications detected in the predetermined time in the past;

generating track-coupling candidates so as to generate hypotheses when it is determined that variations occur in identifications;

selecting individual hypotheses from among the generated hypotheses when it is determined that variations occur in identifications, while selecting individual hypotheses from among the stored hypotheses when it is determined that no variations occur in identifications; and

upon estimating the most-probable hypothesis among the generated hypotheses when it is determined that variations occur in identifications, storing the most-probable hypothesis together with all the other generated hypotheses.

27. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 24 further comprising:

correlating attributes of mobile bodies detected in the predetermined time in the past to track-coupling candidates so as to generate sets of track-coupling candidate/identification pairs, combining track-coupling candidates, correlated to attributes, and identifications of mobile bodies, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying a predetermined condition;

calculating an attribute likelihood representing a likelihood of indicating the same mobile body with attributes, correlated to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses;

integrating attribute likelihoods for each track-coupling candidate/identification pair, thus calculating an attribute likelihood regarding each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods and attribute likelihoods of hypotheses.

28. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 24 further comprising:

correlating mobility values of mobile bodies detected in the predetermined time in the past to track-coupling candidates so as to generate sets of track-coupling candidate/identification pairs combining track-coupling candidates correlated to mobility values and identifications of mobile bodies, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying the predetermined condition;

calculating a mobility likelihood representing a likelihood of indicating the same mobile body with mobility values correlated to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses;

integrating mobility likelihoods for each track-coupling candidate/identification pair, thus calculating a mobility likelihood for each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods and mobility likelihoods of hypotheses.

29. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 24 further comprising:

calculating a track-coupling likelihood representing a likelihood of connecting tracks included in each of track-coupling candidates in track-coupling candidate/identification pairs ascribed to each of the selected hypotheses; and

estimating the most-probable hypothesis based on identification likelihoods and track-coupling likelihoods of hypotheses.

30. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 29 further comprising:

calculating track-coupling likelihoods based on detection times and positional coordinates of mobile bodies used to define tracks.

31. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 23 further comprising:

calculating track-coupling likelihoods representing likelihoods of connecting tracks included in track-coupling candidates so as to select track-coupling candidates whose track-coupling likelihoods are equal to or above a threshold; and

generating hypotheses by use of the selected track-coupling candidates.

32. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 23 further comprising:

storing environmental information by use of a position of an obstruction disposed in a tracking area and a transient time of a mobile body passing by the obstruction;

calculating a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate;

estimating a transient time of the mobile body from a track disappearance time to a next track appearance time based on the environmental information; and

precluding track-coupling candidates each having the estimated transient time longer than the real transient time from track-coupling candidates for use in generation of hypotheses.

33. The non-transitory computer-readable storage medium implementing a mobile body track identification method according to claim 23 further comprising:

storing environmental information representing a transient time of a mobile body along a passable path in a tracking area;

calculating a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate;

estimating a transient time of a mobile body from a track disappearance time to a next track appearance time based on the environmental information; and

precluding track-coupling candidates each having the estimated transient time longer than the real transient time from track-coupling candidates for use in generation of hypotheses.