Abstract
Multi-Object Tracking (MOT) is a challenging research area in computer vision with significant practical applications. With the advent of deep neural networks, significant progress has been made in MOT, and Qdtrack has become a widely used algorithm due to its relatively simple structure and high performance. However, accurate target tracking in complex scenes with mutual occlusion, motion blur and complex backgrounds is still a significant challenge. To address the problem of low target tracking accuracy in complex scenes, this paper proposes an end-to-end multi-object tracking algorithm based on attention feature fusion. First, a new lightweight attention module is introduced into the backbone network, which enhances the ability of the network to capture key information and locate targets without increasing the computational complexity. Second, the feature pyramid structure is improved to reduce the loss of features in the fusion process and improve the feature expression ability of the model. Finally, the Intersection over Union (IoU) of the original model is optimised and the regression ability of the bounding box is improved using polyloss to optimise the cross-entropy loss. Experimental results on the MOT16 and MOT17 benchmarks show that the proposed algorithm effectively improves the accuracy and robustness of multi-object pedestrian tracking compared to other algorithms, and has better tracking performance.
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Zhou, Y., Du, Z., Wang, D. (2023). Pedestrian Multi-object Tracking Algorithm Based on Attention Feature Fusion. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_9
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