Abstract
Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built-in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the response of KCF to identify whether KCF loses the target. A more specific detection area of the detection module is obtained through the estimated location provided by the tracking module. With the above operations, the scanning area of object detection is reduced, and a full frame search is required in the detection module if objects fails to be tracked in the tracking module. Comparative experiments were conducted on the object tracking benchmark (OTB) and the results showed that the tracking speed and accuracy was improved. Further, the TLD tracker performed better in different challenging scenarios with the proposed method, such as motion blur, occlusion, and environmental changes. Moreover, the improved TLD achieved outstanding tracking performance compared with common tracking algorithms.
Highlights
IntroductionAs an important research field of computer vision, is a fundamental part of many computer vision systems, such as military navigation, human–computer interaction, unmanned aerial vehicle video analysis, dynamic behavioral recognition, intelligent medical treatment, and so on (see Figure 1) [1,2,3]
Visual object tracking, as an important research field of computer vision, is a fundamental part of many computer vision systems, such as military navigation, human–computer interaction, unmanned aerial vehicle video analysis, dynamic behavioral recognition, intelligent medical treatment, and so on [1,2,3]
TLD provides an effective framework for solving long-term tracking problems
Summary
As an important research field of computer vision, is a fundamental part of many computer vision systems, such as military navigation, human–computer interaction, unmanned aerial vehicle video analysis, dynamic behavioral recognition, intelligent medical treatment, and so on (see Figure 1) [1,2,3]. Given the location and extent of an arbitrary object, the task of visual object tracking is to determine the object’s location with the best possible accuracy in the following frames [4]. Algorithms 2019, 12, 6 FOR PEER REVIEW (a) Unmanned aerial vehicle video analysis (b) Dynamic hand gesture recognition
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