EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Robust, accurate and efficient pedestrian tracking in surveillance scenes is a critical task in many intelligent visual security systems and robotic vision applications. The usual Markov chain based tracking algorithms suffer from error accumulation problem in which the tracking drifts from the objects as time passes. To minimize the accumulation of tracking errors, in this paper we propose to incorporate the semantic information about each observation in the Markov chain model. We thus obtain pedestrian tracking as a temporal Markov chain with two hidden states, called hidden-latent temporal Markov chain (HL-TMC). The hidden state is used to generate the estimated observations during the Markov chain transition process and the latent state represents the semantic information about each observation. The hidden state and the latent state information are then used to obtain the optimum observation, which is the pedestrian. Use of latent states and the probabilistic latent semantic analysis (pLSA) handles the tracking error accumulation problem and improves the accuracy of tracking. Further, the proposed HL-TMC method can effectively track multiple pedestrians in real time. The performance evaluation on standard benchmarking datasets such as CAVIAR, PETS2006 and AVSS2007 shows that the proposed approach minimizes the accumulation of tracking errors and is able to track multiple pedestrians in most of the surveillance situations.