1 / 20

An Adaptive Learning Method for Target Tracking across Multiple Cameras

An Adaptive Learning Method for Target Tracking across Multiple Cameras. Kuan-Wen Chen, Chih -Chuan Lai, Yi-Ping Hung, Chu-Song Chen National Taiwan University CVPR2008 Reporter: Chia-Hao Hsieh 2009/1/19. Outline. Introduction Visual cues for tracking across camera

derex
Download Presentation

An Adaptive Learning Method for Target Tracking across Multiple Cameras

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Adaptive Learning Method for Target Tracking across Multiple Cameras Kuan-Wen Chen, Chih-Chuan Lai, Yi-Ping Hung, Chu-Song Chen National Taiwan University CVPR2008 Reporter: Chia-Hao Hsieh 2009/1/19

  2. Outline • Introduction • Visual cues for tracking across camera • Spatio-Temporal Relationships • Brightness Transfer Functions • Experimental Results

  3. Introduction • Adaptive learning method • Tracking targets across multiple cameras with disjoint views • Using prior knowledge • Camera network topology • Sudden lighting changes

  4. Spatio-Temporal Relationships • Prior knowledge of camera network topology Which pair of cameras are adjacent The blind regions are closed or open • Advantage • Decrease computation complexity • Help remove the redundant links

  5. Spatio-Temporal Relationships • Batch + Adaptive learning method • Batch learning phase • Estimate entry/exit zones for each single image • Model each entry/exit zones as a GMM, and use EM to estimate parameter of GMM • Adaptive learning phase • Learn the transition probability for each possible link

  6. Spatio-Temporal Relationships • transition probability • Valid link • If exceeds double of the median value

  7. Spatio-Temporal Relationships • Problems • Misclassify two zones into one single zone • Update the entry/exit zones by using on-line K-means approximation • Propose some operators • Zone Addition, Zone Merging, Zone Split

  8. Brightness Transfer Functions • In [7], m x m matrix • The appearance is modeled as an m-bin histogram • Propose an unsupervised learning method • Low dimensional subspace • Using spatio-temporal information and Markov chain Monte Carlo (MCMC) sampling [7] Tracking objects across cameras by incrementally learning inter-camera color calibration and patterns of activity. In ECCV, 2006

  9. Brightness Transfer Functions • Model • Normalized cumulative histogram Hi, Hj. • The percentage of image points in Oi with brightness less than or equal to Bi is equal to the percentage of image points in Oj with brightness less than or equal to Bj. • fij is the BTF for every pair of observations Oi and Oj in the training set

  10. Brightness Transfer Functions • Learning • Probabilistic Principal Component Analysis PPCA • fij can be written as • BTF can be learnt with less data The average reconstruction error decreases when the number of learning data increases

  11. Criterion for BTF estimation • The transformed histogram gives a much better match as compared to direct histogram matching A correct BTF learnt by using correct correspondences would have a more diverse reconstruction error distribution and lower errors than the one learnt by using incorrect correspondences

  12. Criterion for BTF estimation • criterion p(π) for BTF estimation • similarity(pairi): the similarity score of the ith corresponding pair, which is calculated by (1-reconstruction_error(pairi))

  13. Spatio-temporal information and MCMC sampling • BTF is learnt • without hand-labeled correspondence • by sampling from the training data set • By choosing the best BTF according to the criterion • NOT practical to sample all of the permutations directly

  14. Spatio-temporal information and MCMC sampling • For example • n observations • n! matching permutations • But, n pairs at most the correct correspondence • Sample by using Markov Chain Monte Carlo and Metropolis-Hastings algorithm

  15. Experimental Results

  16. Experimental Results Makris’s method This paper

  17. Experimental Results faster learning rate. Gilbert and Bowden’s method never learns a stable BTF in the testing period

  18. Experimental Results • Tracking Results The overall tracking accuracy is 89.4% by using unseen ground-truth of half an hour

  19. Experimental Results Outdoor environment Performs well and achieves high tracking accuracy in both indoor and outdoor environment

  20. Conclusion • Unlike the other approaches assuming that the monitored environments remain unchanged • Incrementally refine the clustering results of the entry/exit zones • Learns the appearance relationship in a short period of time • Combing the spatio-temporal information and efficient MCMC sampling • Can re-build the appearance relationship models soon after sudden lighting changes

More Related