Tracking and data association
Computational Optimization and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Optimization and Applications
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Multiple Camera Fusion for Multi-Object Tracking
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Fusion of Multiple Camera Views for Kernel-Based 3D Tracking
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
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
Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data
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We developed two methods for tracking multiple objects using several camera views. The methods use the Multiple Hypothesis Tracking (MHT) framework to solve both the across-view data association problem (i.e., finding object correspondences across several views) and the across-time data association problem (i.e., the assignment of current object measurements to previously established object tracks). The "tracking-reconstruction method" establishes two-dimensional (2D) objects tracks for each view and then reconstructs their three-dimensional (3D) motion trajectories. The "reconstruction-tracking method" assembles 2D object measurements from all views, reconstructs 3D object positions, and then matches these 3D positions to previously established 3D object tracks to compute 3D motion trajectories. For both methods, we propose techniques for pruning the number of association hypotheses and for gathering track fragments. We tested and compared the performance of our methods on thermal infrared video of bats using several performance measures. Our analysis of video sequences with different levels of densities of flying bats reveals that the reconstruction-tracking method produces fewer track fragments than the trackingreconstruction method but creates more false positive 3D tracks.