Improving multiple pedestrians tracking with semantic information

This work presents an interacting multiple pedestrian tracking method for monocular systems that incorporates a prior knowledge about the environment and about interactions between targets. Pedestrian motion being ruled by both environment and social aspects, we model these complex behaviors by cons...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2014-12, Vol.8 (Suppl 1), p.113-123
Main Authors: Madrigal, Francisco, Hayet, Jean-Bernard, Lerasle, Frédéric
Format: Article
Language:English
Subjects:
Computer Imaging
Computer Science
Computer Vision and Pattern Recognition
Image Processing
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
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Summary:This work presents an interacting multiple pedestrian tracking method for monocular systems that incorporates a prior knowledge about the environment and about interactions between targets. Pedestrian motion being ruled by both environment and social aspects, we model these complex behaviors by considering four cases of motion: going straight, finding one’s way, walking around and standing still. They are combined within an interacting multiple model particle filter strategy. We model targets interactions with social forces included as potential functions in the weighting process of the particle filter. We use different social force setups within each motion model to handle high-level behaviors (collision avoidance, flocking...). We evaluate our algorithm on challenging datasets and show that such semantic information improves the tracker performance compared to the literature.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-014-0710-z