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

Yong Gong; Gautam Srivastava

doi:10.4108/eetsis.v9i6.2591

Authors

Yong Gong Wuhan Polytechnic University
Gautam Srivastava Brandon University

DOI:

https://doi.org/10.4108/eetsis.v9i6.2591

Keywords:

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

Abstract

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.

References

Ammar, S. , Bouwmans, T. , Zaghden, N. , & Neji, M. (2020). Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance. IET Image Processing, 14(8), 1490-1501.

Liu, S., Wang, S., Liu, X., Lin, C. T., & Lv, Z. (2021) Fuzzy Detection aided Real-time and Robust Visual Tracking under Complex Environments. IEEE Transactions on Fuzzy Systems, 29(1), 90-102

Li, B. (2021). Auto-Extraction Simulation of Visual Saliency Image Region in Video Sequence. Computer Simulation,38(07), 447-450.

Yu, Y., Wang, H., Liu, S., Guo, L. & Li, Y. (2021). Distributed multi-agent target tracking: a nash-combined adaptive differential evolution method for uav systems. IEEE Transactions on Vehicular Technology, .2021, 70(8), 8122-8133.

Zhang, Y. , Wei, Y F. , Tao, J T. , & Li, J. (2021). Multiple Object Tracking and Motion Trajectory Prediction of Basketball Players. Chinese Journal of Sports Medicine, 40(10), 800-809.

Xiu, J j. , Han L L, . Dong, K. , Li, Q F. (2020). Study on Correlation and Tracking Algorithm of Space Dense Group Targets. Fire Control & Command Control, 45(8), 51-56.

Rk, A. , Rt, A. , Nm, B. , Gsc, D. , Trg, E. , & Nnx, F. . (2021). A secured distributed detection system based on IPFS and blockchain for industrial image and video data security. Journal of Parallel and Distributed Computing,2(22), 128-143.

Cao, X. Z. , & Gadekallu, T. R. . (2022). Construction of sports safety information mining platform based on multimedia data sharing technology, Mobile Networks and Applications, 5(2), 1-10.

Ma, L. , Li, Y. , Li, J. , Tan, W. , Yu, Y. , & Chapman, M. A. . (2021). Multi-scale point-wise convolutional neural networks for 3d object segmentation from lidar point clouds in large-scale environments. IEEE Transactions on Intelligent Transportation Systems, 22(2), 821-836.

Jin, Y. & Jin, L. Z. (2020). Multi-target (Pedestrian) Detection Algorithm Based on Mobile U-Net. Industrial Control Computer,33(03), 81-83.

Alenezi, F. , & Ganesan, S. . (2021). Geometric-pixel guided single-pass convolution neural network with graph cut for image dehazing. IEEE Access, 9, 29380-29391.

Lv, J. , Sun, Q. , Li, Q. , & Moreira-Matias, L. . (2020). Multi-scale and multi-scope convolutional neural networks for destination prediction of trajectories. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3184-3195.

Das, D. , Nayak, D. R. , Dash, R. , Majhi, B. , & Zhang, Y. D. (2020). H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition. IET Image Processing, 14(9), 1794-1805.

Huang, Y. , Zhang, Y. , Zhao, Y. , Shi, P. , & Chambers, J. A. (2020). A novel outlier-robust Kalman filtering framework based on statistical similarity measure. IEEE Transactions on Automatic Control, 66(6), 2677-2692.

Cao, L. , Zheng, D. , Zhao, Z. , Wang, T. , Wang, D. , Fu, C. , & Gu, J. (2021). Convex Variational Inference for Multi-Hypothesis Fractional Belief Propagation Based Data Association in Multiple Target Tracking Systems. IEEE Sensors Journal, 21(17), 19121-19133.

Olech, U. P., Spytkowski, M., Kwanicka, H., & Michalewicz, Z. (2021). Hierarchical data generator based on tree-structured stick breaking process for benchmarking clustering methods. Information Sciences, 554, 99-119.

Adhami, M. H. , & Ghazizadeh, R. (2021). Secure multiple target tracking based on clustering intersection points of measurement circles in wireless sensor networks. Wireless Networks, 27(2), 1233-1249.

Liu, S., Wang, S., Liu, S., Gandomi, A. H., Daneshmand, M, Muhammad, K, & Albuquerque, V. H. C. (2021) Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring, IEEE Transactions on Multimedia, 23, 2188-2198

Liu, S., Xu, X., Zhang, Y., et al. (2022) A Reliable Sample Selection Strategy for Weakly-supervised Visual Tracking, IEEE Transactions on Reliability, online first, 10.1109/TR.2022.3162346

Dai, K. , Wang, Y. , Hu, J. S. , Nam, K. , & Yin, C. (2020). Intertarget occlusion handling in multiextended target tracking based on labeled multi-Bernoulli filter using laser range finder. IEEE/ASME Transactions on Mechatronics, 25(4), 1719-1728.

Belhadi A, Djenouri Y, Srivastava G, Djenouri D, Cano A, Lin JC. A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories. IEEE Transactions on Intelligent Transportation Systems. 2020 Sep 23;22(7):4496-506.

Ahmed U, Lin JC, Srivastava G. Deep Fuzzy Contrast-Set Deviation Point Representation and Trajectory Detection. IEEE Transactions on Fuzzy Systems. 2022 Aug 10.