Discovering an Event Taxonomy from Video using Qualitative Spatio-temporal Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Relational Graph Mining for Learning Events from Video
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
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We propose a framework to address the multiple target tracking problem, which is to recover trajectories of targets of interest over time from noisy observations. Due to occlusions by targets and static objects, parallax or other moving objects, foreground regions cannot represents targets faithfully although motion segmentation is usually computationally efficient. We adopt the real Adaboost classifier to generate meaningful candidate rectangles to interpret the foreground regions. Tracks are generated from these candidates according to the smoothness of motion, appearance and model likelihood overtime. To avoid enumerating all possible joint associations, we take a Data Driven Markov Chain Monte Carlo (DD-MCMC) approach which samples the solution space efficiently. The sampling is driven by an informed proposal scheme controlled by a joint probability model combining motion, appearance and model information. Comparative experiments with quantitative evaluations are provided.