.png){kind=icon}
Return
Adaptive Object Tracking via Multi-Angle Analysis Collaboration
.png){kind=icon}
OA
AI
DOI：10.3390/s18113606.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Although tracking research has achieved excellent performance in mathematical angles, it is still meaningful to analyze tracking problems from multiple perspectives. This motivation not only promotes the independence of tracking research but also increases the flexibility of practical applications. This paper presents a significant tracking framework based on the multi-dimensional state-action space reinforcement learning, termed as multi-angle analysis collaboration tracking (MACT). MACT is comprised of a basic tracking framework and a strategic framework which assists the former. Especially, the strategic framework is extensible and currently includes feature selection strategy (FSS) and movement trend strategy (MTS). These strategies are abstracted from the multi-angle analysis of tracking problems (observer's attention and object's motion). The content of the analysis corresponds to the specific actions in the multidimensional action space. Concretely, the tracker, regarded as an agent, is trained with Q-learning algorithm and epsilon-greedy exploration strategy, where we adopt a customized rewarding function to encourage robust object tracking. Numerous contrast experimental evaluations on the OTB50 benchmark demonstrate the effectiveness of the strategies and improvement in speed and accuracy of MACT tracker.
Keywords:
visual tracking
multi-angle analysis
multi-dimensional state-action space
reinforcement learning
collaboration
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.2W
Citations:
20.9W
Organization
Cited Papers
ShareSaveDiscovery of the water scavenger beetle genus Brownephilus Mouchamps in Turkey (Coleoptera, Hydrophilidae, Hydrophilini).png)
ZooKeysOAAI
ZooKeysIF0
OAAIShareSaveShareSaveShareSaveShareSave
View more