Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
Tracking using motion patterns for very crowded scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Coherent filtering: detecting coherent motions from crowd clutters
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
| Hi-index | 0.00 |
In this paper, a Random Field Topic (RFT) model is proposed for semantic region analysis from motions of objects in crowded scenes. Different from existing approaches of learning semantic regions either from optical flows or from complete trajectories, our model assumes that fragments of trajectories (called tracklets) are observed in crowded scenes. It advances the existing Latent Dirichlet Allocation topic model, by integrating the Markov random fields (MRF) as prior to enforce the spatial and temporal coherence between tracklets during the learning process. Two kinds of MRF, pairwise MRF and the forest of randomly spanning trees, are defined. Another contribution of this model is to include sources and sinks as high-level semantic prior, which effectively improves the learning of semantic regions and the clustering of tracklets. Experiments on a large scale data set, which includes 40, 000+ tracklets collected from the crowded New York Grand Central station, show that our model outperforms state-of-the-art methods both on qualitative results of learning semantic regions and on quantitative results of clustering tracklets.