Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
International Journal of Computer Vision
KNIGHT/spl trade/: a real time surveillance system for multiple and non-overlapping cameras
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Computer Vision and Image Understanding
Proceedings of the 2007 Summer Computer Simulation Conference
Machine Vision and Applications
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Incremental learning of statistical motion patterns with growing hidden Markov models
IEEE Transactions on Intelligent Transportation Systems
Inter-camera association of multi-target tracks by on-line learned appearance affinity models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Person re-identification in crowd
Pattern Recognition Letters
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Tracking across non-overlapping cameras is a challenging open problem in video surveillance. In this paper, we propose a novel target re-identification method that models movements in non-observed areas with a modified Social Force Model (SFM) by exploiting the map of the site under surveillance. The SFM is developed with a goal-driven approach that models the desire of people to reach specific interest points (goals) of the site such as exits, shops, seats and meeting points. These interest points work as attractors for people movements and guide the path predictions in the non-observed areas. We also model key regions that are potential intersections of different paths where people can change the direction of motion. Finally, the predictions are linked to the trajectories observed in the next camera view where people reappear. We validate our multi-camera tracking method on the challenging i-LIDS dataset from the London Gatwick airport and show the benefits of the Multi-Goal Social Force Model.