CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
Physically-based motion models for 3D tracking: A convex formulation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper addresses the problem of tracking a large number of targets moving in 3D space using multiple calibrated video cameras. Most visual details of the targets are lost in the captured images because of limited image resolution, and the remainder can be easily corrupted due to frequent occlusion, which makes it difficult to determine both across-view and temporal correspondences. We propose a fully automatic tracking system that is capable of detecting and tracking a large number of flying targets in a 3D volume. The system includes a 3D tracking method in the framework of particle filter. Different from previous 2D tracking methods, the proposed method models the 3D attributes of targets and furthest collects weak visual information from multiple views, which makes the tracker robust against occlusion and distraction. The ambiguities in stereo matching when initializing trackers are handled by an effective multiple hypothesis generation and verification mechanism. The whole system is fully automatic in dealing with variable number of targets and robust against detection and matching errors. Our system has successfully been used by biologists to recover the 3D trajectories of hundreds of fruit flies flying freely in a 3D volume.