Journal article
Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes
IEEE transactions on pattern analysis and machine intelligence, v 34(5), pp 987-1002
May 2012
PMID: 21844618
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Tracking pedestrians is a vital component of many computer vision applications, including surveillance, scene understanding, and behavior analysis. Videos of crowded scenes present significant challenges to tracking due to the large number of pedestrians and the frequent partial occlusions that they produce. The movement of each pedestrian, however, contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents over the entire video) that exhibits an underlying spatially and temporally varying structured pattern. In this paper, we present a novel Bayesian framework for tracking pedestrians in videos of crowded scenes using a space-time model of the crowd motion. We represent the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video. Using this unique representation, we predict the next local spatio-temporal motion pattern a tracked pedestrian will exhibit based on the observed frames of the video. We then use this prediction as a prior for tracking the movement of an individual in videos of extremely crowded scenes. We show that our approach of leveraging the crowd motion enables tracking in videos of complex scenes that present unique difficulty to other approaches.
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Details
- Title
- Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes
- Creators
- Louis Kratz - Drexel UniversityKo Nishino - Drexel University
- Publication Details
- IEEE transactions on pattern analysis and machine intelligence, v 34(5), pp 987-1002
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000301747400012
- Scopus ID
- 2-s2.0-84859198469
- Other Identifier
- 991019168444404721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic