Multi-Pose Multi-Target Tracking for Activity Understanding

Hamid Izadinia, Varun Ramakrishna, Kris M. Kitani, and Daniel Huber
IEEE Workshop on the Applications of Computer Vision, January, 2013.


Download
  • Adobe portable document format (pdf) (2MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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 M. 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 M 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."
}