Conference proceeding
Tracking with Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes
2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp 693-700
01 Jan 2010
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Tracking individuals in extremely crowded scenes is a challenging task, primarily due to the motion and appearance variability produced by the large number of people within the scene. The individual pedestrians, however, collectively form a crowd that exhibits a spatially and temporally structured pattern within the scene. In this paper, we extract this steady-state but dynamically evolving motion of the crowd and leverage it to track individuals in videos of the same scene. We capture the spatial and temporal variations in the crowd's motion by training a collection of hidden Markov models on the motion patterns within the scene. Using these models, we predict the local spatio-temporal motion patterns that describe the pedestrian movement at each space-time location in the video. Based on these predictions, we hypothesize the target's movement between frames as it travels through the local space-time volume. In addition, we robustly model the individual's unique motion and appearance to discern them from surrounding pedestrians. The results show that we may track individuals in scenes that present extreme difficulty to previous techniques.
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Details
- Title
- Tracking with Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes
- Creators
- Louis Kratz - Drexel UniversityKo Nishino - Drexel UniversityIEEE
- Publication Details
- 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp 693-700
- Series
- IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000287417500089
- Scopus ID
- 2-s2.0-77956005563
- Other Identifier
- 991019168738104721
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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
- Computer Science, Software Engineering
- Imaging Science & Photographic Technology
- Mathematics, Applied