1. Stable Multi-Target Tracking in Real-Time Surveillance Video
Active Vision Group
Department of Engineering Science University of Oxford
Ben Benfold Ian Reid
CVPR 2011

2. OUTLINE Introduction Sliding Window Tracking Evaluation Conclusions
Observations
Data Association
Output Generation
Evaluation
Conclusions

3. OUTLINE Introduction Sliding Window Tracking Evaluation Conclusions
Observations
Data Association
Output Generation
Evaluation
Conclusions

4. Introduction Tradition method vs Paper method Approach
Maintain targets vs Stable location estimates
ad-HOG vs MDL
Approach
Multi-threaded
Combines asynchronous HOG detection
Simultaneous KLT tracking
MCMCDA

5. Introduction Recent Work
Feed-forward systems which use only current and past observations to estimate the current state
Data association based methods which also use future information to estimate the current state

6. Introduction MCMC MCMCDA tracking systems
Tracking a single or fixed number of targets
Multi-target tracking
MCMCDA tracking systems
Associating object detections resulting from background subtraction
A boosted Haar classifier cascade
Object detections and Motion estimations

7. Introduction Main contribution The development of a tracking model
Treatment of false positive detections
A comprehensive evaluation of the tracker on multiple datasets using the standard CLEAR MOT evaluation criteria

8. OUTLINE Introduction Sliding Window Tracking Evaluation Conclusions
Observations
Data Association
Output Generation
Evaluation
Conclusions

9. Observation Make the tracking algorithm robust Object detections
the most recent six seconds of video
Object detections
HOG
Trained a detector using head images
Interval
200 milliseconds for PAL video
1200 milliseconds for 1080p video

10. Observation Motion estimates
Pyramidal Kanade-Lucas-Tomasi(KLT) tracking

11. Observation Pyramidal KLT tracking Provide robustness
Up to four corner features
Tracked both forwards and backwards
in time from detections for up to s seconds
S = 4 sec
more precise than mean-shift

12. OUTLINE Introduction Sliding Window Tracking Evaluation Conclusions
Observations
Data Association
Output Generation
Evaluation
Conclusions

13. Data Association Hypothesis Hi
Divides the set of detections D
Disjoint subsets T1, T TJ
Tj corresponding to a single person
Not every detection that occurs is a true positive
,represent Tj being a genuine pedestrian track
,if we believe Tj is a track of false positives

14. Data Association
MCMC sampling to efficiently explore the space of data associations by generating a sequence H0,H1,H2, . . .

15. Data Association

16. Data Association Likelihood Function p(Hί)
conditional probability function
An approach based on the principles of MDL

17. Data Association 1 Gibbs sampling be the n-th detection in a track Tj
is a prior over the different track types
be the n-th detection in a track Tj

18. Data Association Sn : the scale of the detection
Xn: the location within the frame
₥n: an approximation to the KLT motion

19. Data Association
Detection Scales
Initial Step:
Iteration Step:

20. Data Association Image Location Initial Step:
assumed that the locations of both pedestrians and false-positives are uniformly distributed around the image
the probability density of xn depends on the image area a relative to the object size in pixels

21. Data Association Image Location
First make a prediction based on a constant velocity model
the error p is still large partly due to the cyclic gait motion
humans often change direction when in crowds

22. Data Association Image Location The full KLT motion estimates
Kalman filter

23. Data Association Image Location
α : The possibility that a tracked KLT feature fails completely
: The possibility of failure after for seconds
A mixture of the prior and posterior distributions

24. Data Association Motion Magnitude The last observation considered
the motion magnitude histogram
distinguish between false positives and true positives
Bins with boundaries
1/8, 1/4 , 1/2 pixels per frame

25. Data Association Sampling
Three types of move which can be made during the sampling process
the first two moves effect the state of the DA
the third has the potential to change the type of a track
First : Swap
Second : Switch
Third : M-H acceptance function

26. Data Association
First , Second

27. Data Association Third Although Metropolis-Hastings is good
We prefer stable output rather than samples
Keep track of the most likely hypothesis
Output the local maximum

28. Data Association Parameter Estimation Learned automatically
based on that of Ge and Collins
Interleaving the MCMCDA sampling with additional Metropolis-Hastings updates of the parameters
Provided the parameters are initialised
allowing some tracks to be correctly associated
converge to a maximum of the likelihood function

29. Data Association Parameter Estimation Longer than data association
over an hour or two
most datasets are too short for this
slow down the video used for training

30. OUTLINE Introduction Sliding Window Tracking Evaluation Conclusions
Observations
Data Association
Output Generation
Evaluation
Conclusions

31. Output Generation The final stage
generate estimates for the object location in each frame
stimate the true image locations
Detection do not occur in every frame

32. OUTLINE Introduction Sliding Window Tracking Evaluation Conclusions
Observations
Data Association
Output Generation
Evaluation
Conclusions

33. Evaluation The Multiple Object Tracking Precision (MOTP)
objects are located using the intersection of the estimated region with the ground truth region
The Multiple Object Tracking Accuracy (MOTA)
takes into account false positives,false negatives and identity switches

34. actual class (expectation)
Evaluation
actual class (expectation)
predicted class (observation)
tp (true positive) Correct result
fp (false positive) Unexpected result
fn (false negative) Missing result
tn (true negative) Correct absence of result

35. Evaluation

36. Evaluation

37. Evaluation

38. OUTLINE Introduction Sliding Window Tracking Evaluation Conclusions
Observations
Data Association
Output Generation
Evaluation
Conclusions

39. Conclusion Described and demonstrated a scalable real- time system
The use of MCMCDA makes the system robust
Our efficient approach provides general tracking performance comparable to that of similar systems

40. Thanks For Your Listening