Title

Multi-Pose Multi-Target Tracking For Activity Understanding

Abstract

We evaluate the performance of a widely used tracking-by-detection and data association multi-target tracking pipeline applied to an activity-rich video dataset. In contrast to traditional work on multi-target pedestrian tracking where people are largely assumed to be upright, we use an activity-rich dataset that includes a wide range of body poses derived from actions such as picking up an object, riding a bike, digging with a shovel, and sitting down. For each step of the tracking pipeline, we identify key limitations and offer practical modifications that enable robust multi-target tracking over a range of activities. We show that the use of multiple posture-specific detectors and an appearance-based data association post-processing step can generate non-fragmented trajectories essential for holistic activity understanding. © 2013 IEEE.

Publication Date

4-4-2013

Publication Title

Proceedings of IEEE Workshop on Applications of Computer Vision

Number of Pages

385-390

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/WACV.2013.6475044

Socpus ID

84875630667 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84875630667

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