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Carnegie Mellon Robotics Institute

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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.

Keywords

Multi-Target Tracking, Activity Recognition

Notes

Note: This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agree- ment Number W911NF-10-2-0061.

Text Reference

Hamid Izadinia, Varun Ramakrishna, Kris Kitani, and Daniel Huber, "Multi-Pose Multi-Target Tracking for Activity Understanding," IEEE Workshop on the Applications of Computer Vision, January, 2013.

BibTeX Reference

@inproceedings{Ramakrishna_2013_7388,
   author = "Hamid Izadinia and Varun Ramakrishna and Kris Kitani and Daniel Huber",
   title = "Multi-Pose Multi-Target Tracking for Activity Understanding",
   booktitle = "IEEE Workshop on the Applications of Computer Vision",
   month = "January",
   year = "2013",
   Notes = "This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agree- ment Number W911NF-10-2-0061."
}

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