cvpr cvpr2013 cvpr2013-440 cvpr2013-440-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Tobias Baumgartner, Dennis Mitzel, Bastian Leibe
Abstract: Current pedestrian tracking approaches ignore important aspects of human behavior. Humans are not moving independently, but they closely interact with their environment, which includes not only other persons, but also different scene objects. Typical everyday scenarios include people moving in groups, pushing child strollers, or pulling luggage. In this paper, we propose a probabilistic approach for classifying such person-object interactions, associating objects to persons, and predicting how the interaction will most likely continue. Our approach relies on stereo depth information in order to track all scene objects in 3D, while simultaneously building up their 3D shape models. These models and their relative spatial arrangement are then fed into a probabilistic graphical model which jointly infers pairwise interactions and object classes. The inferred interactions can then be used to support tracking by recovering lost object tracks. We evaluate our approach on a novel dataset containing more than 15,000 frames of personobject interactions in 325 video sequences and demonstrate good performance in challenging real-world scenarios.
[1] M. Andriluka, S. Roth, and B. Schiele. Monocular 3D Pose Estimation and Tracking by Detection. In CVPR, 2010.
[2] M. Bajracharya, B. Moghaddam, A. Howard, S. Brennan, and L. Matthies. A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle. IJRS, 2009.
[3] M. Bansal, S. H. Jung, B. Matei, J. Eledath, and H. S. Sawhney. A real-time pedestrian detection system based on structure and appearance classification. In ICRA, 2010.
[4] W. Choi and S. Savarese. A Unified Framework for MultiTarget Tracking and Collective Activity Recognition. In ECCV, 2012.
[5] D. Damen and D. Hogg. Detecting Carried Objects in Short Video Sequences. In ECCV, 2008.
[6] A. Ess, B. Leibe, K. Schindler, and L. Van Gool. Robust Multi-Person Tracking from a Mobile Platform. PAMI, 2009.
[7] P. Felzenszwalb, B. Girshick, D. McAllester, and D. Ramanan. Object Detection with Discriminatively Trained PartBased Models. PAMI, 2010.
[8] A. Geiger, M. Roser, and R. Urtasun. Efficient Large-Scale Stereo Matching. In ACCV, 2010.
[9] C. Keller, D. Fernandez-Llorca, and D. Gavrila. Dense Stereo-based ROI Generation for Pedestrian Detection. In DAGM, 2009.
[10] C.-H. Kuo, C. Huang, and R. Nevatia. Multi-Target Tracking by On-Line Learned Discriminative Appearance Models. In CVPR, 2010.
[11] B. Lau, K. Arras, and W. Burgard. Tracking Groups of People with a Multi-Hypothesis Tracker. In ICRA, 2009.
[12] B. Leibe, K. Schindler, and L. Van Gool. Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles. PAMI, 2008.
[13] D. Mitzel and B. Leibe. Taking Mobile Multi-Object Tracking to the Next Level: People, Unknown Objects, and Carried Items. In ECCV, 2012.
[14] J. Pearl. Fusion, Propagation, and Structuring in Belief Networks. Art. Intell., 1986.
[15] S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool. You’ll Never Walk Alone: Modeling Social Behavior for MultiTarget Tracking. In ICCV, 2009.
[16] S. Pellegrini, A. Ess, and L. Van Gool. Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings. In ECCV, 2010.
[17] K. Smith, P. Quelhas, and D. Gatica-Perez. Detecting abandoned luggage items in a public space. In PETS, 2006.
[18] A. Vedaldi and S. Soatto. Quick Shift and Kernel Methods for Mode Seeking. In ECCV, 2008.
[19] C. Wojek, S. Walk, S. Roth, and B. Schiele. Monocular 3D Scene Understanding with Explicit Occlusion Reasoning. In CVPR, 2011.
[20] K. Yamaguchi, A. Berg, L. Ortiz, and T. Berg. Who are you with and where you are going? In CVPR, 2011. 333666666533