Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/WACV.2013.6475044 | WACV |
Keywords | DocType | Citations |
data association multi-target tracking,appearance-based data association,robust multi-target tracking,multi-target pedestrian tracking,key limitation,activity-rich dataset,Multi-pose multi-target tracking,wide range,activity-rich video dataset,tracking pipeline,holistic activity understanding | Conference | 2 |
PageRank | References | Authors |
0.38 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Varun Ramakrishna | 1 | 360 | 9.87 |
| Daniel F. Huber | 2 | 699 | 46.34 |
| Kris M. Kitani | 3 | 630 | 72.32 |
| Hamid Izadinia | 4 | 164 | 11.16 |