In the approaching tracking process, we get infrared images from far to near, and the target in the images varies from small to big. The main problems in the tracking process are the large variety of scale, the change of target’s posture and intensity, the collusion of interferences. This paper focuses on the whole tracking and detection process when the target and inferences exist at the same time and block each other. We propose an approach which includes several cues. Firstly, according to the amount of target pixels, the target is divided into small target and shaped target. Then we use the information of the target displacement rate to track and detect the small target which only has several pixels and is unstable. For the shaped target, we can make use of the target’s contrast information. Lastly, we validate our method by designing an experiment which simulates the entire tracking and detection process. The test shows that our method improve the performance.
In the approaching tracking process, we get infrared images from far to near, and the target in the images varies from small to big. The main problems in the tracking process are the large variety of scale, the change of target’s posture and intensity, the collusion of interferences. This paper focuses on the whole tracking and detection process when the target and inferences exist at the same time and block each other. We propose an approach which includes several cues. Firstly, according to the amount of target pixels, the target is divided into small target and shaped target. Then we use the information of the target displacement rate to track and detect the small target which only has several pixels and is unstable. For the shaped target, we can make use of the target’s contrast information. Lastly, we validate our method by designing an experiment which simulates the entire tracking and detection process. The test shows that our method improve the performance.
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