Multi-target trajectory tracking in multi-frame video images of basketball sports based on deep learning




deep learning, basketball sports video, multi-objective, trajectory tracking, YOLOv3 algorithm, data association


INTRODUCTION: There is occlusion interference in the multi-target visual tracking process of basketball video images, which leads to poor accuracy of multi-target trajectory tracking. This paper studies the multi-target trajectory tracking method in multi-frame video images of basketball sports based on deep learning.

OBJECTIVES: Aiming at the problem of target occlusion in the tracking process and the problem of trajectory tracking anomaly caused by target occlusion, a modified algorithm is proposed.

METHODS:  The method is divided into two parts: detection and tracking. In the detection part, the YOLOv3 algorithm in deep learning technology is used to detect each target in the video, and the original YOLOv3 backbone network Darknet-53 is replaced by the lightweight backbone network MobileNetV2 to extract the target features.

RESULTS: Based on the target detection results, the Kalman filter is used to predict the next position and bounding box size of the target to obtain the target trajectory prediction results according to the current target position, then a hierarchical data association algorithm is designed, and multi-target tracking of the same category is completed based on the target appearance feature similarity and feature similarity.

CONCLUSION: The experimental results show that the method can accurately detect the targets in multi-frame video images in basketball sports and obtain high-precision target trajectory tracking results.


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How to Cite

Gong Y, Srivastava G. Multi-target trajectory tracking in multi-frame video images of basketball sports based on deep learning. EAI Endorsed Scal Inf Syst [Internet]. 2022 Oct. 18 [cited 2023 Feb. 5];10(2):e9. Available from: