.png){kind=icon}
.png){kind=icon}
Return
Robust objectness tracking with weighted multiple instance learning algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.02.106.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A novel improved online weighted multiple instance learning algorithm(IWMIL) for visual tracking is proposed. In the IWMIL algorithm, the importance of each sample contributing to bag probability is evaluated based on the objectness estimation with object properties (superpixel straddling). To reduce the computation cost, a coarse-to-fine sample detection method is employed to detect sample for a new arriving frame. Then, an adaptive learning rate, which exploits the maximum classifier score to assign different weights to tracking result and template, is presented to update the classifiers. Furthermore, an object similarity constraint strategy is used to estimate tracking drift. Experimental results on challenging sequences show that the proposed method is robust to occlusion and appearance changes. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Objectness
Weighted multiple instance learning algorithm
Adaptive learning rate
Object similarity constraint
Object tracking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W