Multi-pose multi-target tracking for activity understanding

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-ric...

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Bibliographic Details
Published in2013 IEEE Workshop on Applications of Computer Vision (WACV) pp. 385 - 390
Main Authors Izadinia, H., Ramakrishna, V., Kitani, K. M., Huber, D.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.01.2013
Subjects
Computer vision
Detectors
Histograms
Pedestrians
Picking
Pipelines
Riding
Roads
Robustness
Smoothing methods
Target tracking
Tracking
Trajectory
Workshops
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISBN:9781467350532
1467350532
ISSN:1550-5790
2642-9381
1550-5790
DOI:10.1109/WACV.2013.6475044