Abstract
An improved real-time multi-target tracking algorithm based on the combination of improved YOLOV3-Tiny and deep-sort is proposed. The target tracking environment and target are simple, so it is easier to use the self-built data set to fit and test the vehicle it belongs to. In order to prevent the phenomenon of over-fitting, the affine transformation algorithm is used to expand and transform the data set of heavy trucks. After adding the self-built data set, it has a better effect on the original data set test. By replacing the main network of Yolov3-Tiny with a more concise neural network structure and using the effective combination of Kalman filter and Hungarian algorithm, the whole structure is more portable and fast, easier to deploy to small computers even mobile terminals. Compared with Faster RCNN, MobilenetV2, YOLOV3-Tiny and other algorithms on self-built data sets and Pcal VOC data sets, the proposed algorithm has higher real-time performance and accuracy, and robustness. After the neural network is improved, the accuracy of the proposed algorithm is improved by more than 2% compared with the original algorithm. And the computing speed is faster, and the network is more portable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lou, K., Zhu, Z., Ge, H.: Infrared target detection and tracking method based on target motion feature. J. Nanjing Univ. Sci. Technol. 43(4), 455461 (2019). (in Chinese)
Yang, H., Gao, S., Wu, X., et al.: Online multi-object tracking using KCF-based single-object tracker with occlusion analysis. Multimed. Syst. 26, 655–669
Chen, Y., Zhao, Q., An, Z., Lv, P., Zhao, L.: Distributed multi-target tracking based on the K-MTSCF algorithm in camera networks. IEEE Sens. J. 16(13), 5481–5490 (2016). https://doi.org/10.1109/JSEN.2016.2565263
Xu, X., Yuan, Z., Wang, Y.: Multi-target tracking and detection based on hybrid filter algorithm. In: IEEE Access. https://doi.org/10.1109/ACCESS.2020.3024928
Tian, S., Liu, J., Jin, Y. -D., Deng, C. -Y.: Pedestrian multi-target tracking based on YOLOv3. In: 2020 39th Chinese Control Conference (CCC), Shenyang, China, 2020, pp. 7558–7564 (2020).https://doi.org/10.23919/CCC50068.2020.9189074
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE Press, pp. 6517–6525 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Mobilenetv2: Inverted residuals and linear bottlenecks. mobile networks for classification,detection andsegmentation. CVPR, abs/1801.04381 (2018)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
Howard, A.G., et al.: Efficient convolutional neural networks for mobile vision applications. CVPR, abs/1704.04861 (2017)
Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zicheng, Z. et al. (2022). An Improved Multi-objective Visual Tracking Algorithm. In: Hassanien, A.E., Snášel, V., Chang, KC., Darwish, A., Gaber, T. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021. AISI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-89701-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-89701-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89700-0
Online ISBN: 978-3-030-89701-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)