Name
Abstract | ||
|---|---|---|
This paper addresses the problems of tracking targets which undergo rapid and significant appearance changes. Our starting point is a successful, state-of-the-art tracker based on an adaptive coupled-layer visual model [10]. In this paper, we identify four important cases when the original tracker often fails: significant scale changes, environment clutter, and failures due to occlusion and rapid disordered movement. We suggest four new enhancements to solve these problems: we adapt the scale of the patches in addition to adapting the bounding box, marginal patch distributions are used to solve patch drifting in environment clutter, a memory is added and used to assist recovery from occlusion, situations where the tracker may lose the target are automatically detected, and a particle filter is substituted for the Kalman filter to help recover the target. We demonstrate the advantages of the enhanced tracker over the original tracker using a test toolkit [17]. We demonstrate the advantages of the enhanced tracker over the original tracker, as well as several other state-of-the art trackers from the literature. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCVW.2013.24 | Computer Vision Workshops |
Keywords | Field | DocType |
video signal processing,enhanced adaptive coupled-layer lgtracker++,kalman filter,adaptive coupled-layer visual model,enhanced adaptive coupled-layer lgtracker,kalman filters,video sequences,appearance changes,original tracker,significant scale changes,rapid disordered movement,target tracking,patch drifting,state-of-the-art tracker,significant appearance change,particle filter,state-of-the art tracker,bounding box,object tracking,enhanced tracker,image sequences,marginal patch distribution,marginal patch distributions,occlusion,environment clutter | BitTorrent tracker,Computer vision,Pattern recognition,Visualization,Computer science,Clutter,Particle filter,Kalman filter,Robustness (computer science),Video tracking,Artificial intelligence,Minimum bounding box | Conference |
Volume | Issue | Citations |
2013 | 1 | 17 |
PageRank | References | Authors |
0.62 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjing Xiao | 1 | 44 | 4.10 |
| Rustam Stolkin | 2 | 34 | 6.91 |
| Aleš Leonardis | 3 | 1347 | 103.77 |