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Yangyang Zhang

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Proceedings Article | 25 May 2023 Paper

Research on single object tracking algorithm based on Siamese network and Kalman filter

Yangyang Zhang, Chong Guo, Hongbo Zhu

Proceedings Volume 12636, 126364Y (2023) https://doi.org/10.1117/12.2675178

KEYWORDS: Signal filtering, Detection and tracking algorithms, Tunable filters, Electronic filtering, Video, Feature extraction, Image processing, Visual process modeling, Transform theory, Object detection

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The single object tracking algorithm based on Siamese Network transforms the object tracking problem into the similarity calculation problem of features. By inputting the template and the search frame into the Siamese Network with the same structure and sharing weights, the corresponding features are extracted respectively, and the feature map is output. Then, convolution operation is performed on the feature map to find the area where the search frame is most similar to the template frame, so as to achieve the tracking of a single object. However, this method only uses the appearance feature of the object to track the object, and does not use the motion feature of the object to predict the position of the object. In a scene with multiple objects with similar appearance, it is easy to overlap the spatial positions of similar objects. Or the tracking frame drift phenomenon occurs due to occlusion or other reasons, resulting in the loss of tracking of the object. Kalman Filter is a linear system that can predict the next state of the object based on the current state and known information, and continuously predict the object state through continuous iteration. This feature can be used to predict the motion trajectory of the object, and combining it with the Siamese Network can greatly improve the anti-interference ability of the Siamese Network. This paper uses SiamRPN as the basic network and introduces the Kalman Filter algorithm to predict the object trajectory. The purpose of this paper is to fuse the appearance and motion features of the object, and conduct a comparative experiment with the tracking of a single feature to study the impact of different features on the object tracking performance.

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