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Real-time and Robust Visual Tracking with Scene-perceptual Memory by Yanhua Shao; Xiao Zhang; Kuisheng Liao; Hongyu Chu is a Computer Science article available to read on EtoBox.

What is Real-time and Robust Visual Tracking with Scene-perceptual Memory about?

Unmanned aerial vehicle (UAV) based aerial visual tracking is one of the research hotspots in computer vision. However, the mainstream trackers for UAV still have two shortcomings: (1) the accuracy of correlation filter tracker is greatly improved with more complex model, it impedes accuracy-speed trade-off. (2) object occlusion and camera motion in the aerial tracking scene also seriously restrict the application of aerial tracking. To address these problems, and inspired by AutoTrack tracker, we propose an aerial correlation filtering tracker based on scene-perceptual memory, Fast-AutoTrack. Firstly, to perceive and judge tracking anomalies, such as object occlusion and camera motion, inspired by the peak sidelobe ratio and AutoTrack, a confidence score is designed by perceiving and remembering the changing trend of the confidence and the local historical confidence. Secondly, after tracking anomaly occurring, several search regions are predicted based on the local object motion trend and the Spatio-temporal context information for object re-detection. Finally, to accelerate the model updating, the perceptual hashing algorithm (PHA) is used to obtain the similarity of the search

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Author

Yanhua Shao; Xiao Zhang; Kuisheng Liao; Hongyu Chu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)