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Tracking Multi-objects with Anchor-Free Siamese Network

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Intelligence Science IV (ICIS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 659))

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Abstract

Single object tracker based on siamese neural network have become one of the most popular frameworks in this field for its strong discrimination ability and high efficiency. However, when the task switch to multi-object tracking, the development of siamese-network based tracking methods is limited by the huge calculation cost comes from repeatedly feature extract and excessive predefined anchors. To solve these problems, we propose a siamese box adaptive multi-object tracking (SiamBAN-MOT) method with parallel detector and siamese tracker branch, which implementation in an anchor-free manner. Firstly, ResNet-50 is constructed to extract shared features of the current frame. Then, we design a siamese specific feature pyramid networks (S-FPN) to fuse the multi-scale features, which improves detection and tracking performance with the anchor-free architecture. To alleviate the duplicate feature extraction, RoI align is operated to extract all trajectories’ template feature and search region feature in a single feature map at once. After that, anchor-free based Siamese tracking network outputs the tracking result of each trajectory according to its template and search region feature. Meanwhile, current frame’s detection results are obtained from the detector for the target association. Finally, a simple novel IOU-matching scheme is designed to map the tracking results to the detection results so as to refine the tracking results and suppress the drifts caused by siamese tracking network. Through experimental verification, our method achieves competitive results on MOT17.

This work was supported in part by the National Natural Science Foundation of China under Grant 61871306, Grant 61836009, Grant 62172600, Grant 62077038, by the Innovation Capability Support Program of Shaanxi (Program No. 2021KJXX-08), by the Natural Science Basic Research Program of Shaanxi under Grant No. 2022JC-45 and 2022GY-065, and by the Fundamental Research Funds for the Central Universities under Grant JB211901.

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Correspondence to Jie Feng .

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Hui, B., Feng, J., Yao, Q., Gu, J., Jiao, L. (2022). Tracking Multi-objects with Anchor-Free Siamese Network. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_43

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  • DOI: https://doi.org/10.1007/978-3-031-14903-0_43

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