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A dynamic infrared object tracking algorithm by frame differencing
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AI
DOI：10.1016/j.infrared.2022.104384.png)
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Abstract
En 中文
Infrared object tracking is an important research field of computer vision. Existing tracking-by-detection algorithms usually use appearance information to distinguish between the target and the surrounding environment. However, the complex environment and the colorless or textureless infrared target make object tracking difficult, causing existing algorithms to lose the target. In this paper, to mine the correlation between video frames, we propose a simple and effective way to exploit the motion information by frame differences. Specifically, using adjacent frame differencing, we propose a dynamic infrared object tracking framework to assist existing semantic aware tracking algorithms. Experiments on the Anti-UAV infrared datasets, VOTTIR2015, VOT-TIR2016, PTB-IR and LSOTB-IR datasets show its robustness to the different challenges of the infrared scenes with high efficiency.
Keywords:
Infrared object tracking
Frame differencing
Convolutional neural network
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