Abstract
Multiple object tracking(MOT) is a fundamental problem in video analysis application. Associating unreliable detection in a complex environment is a challenging task. The accuracy of multiple object tracking algorithms is dependent on the accuracy of the first stage object detection algorithm. In this paper, we propose an improved algorithm of IOU Tracker–FIOU Tracker. Our proposal algorithm can overcome the shortcoming of IOU Tracker with a small amount of computing cost that heavily relies on the precision and recall of object detection accuracy. The algorithm we propose is based on the assumption that the motion of background inference is not obvious. We use the average light flux value of the track and the change rate of the light flux value of the center point of the adjacent object as the conditions to determine whether the trajectory is to be retained. The tracking accuracy is higher than the primary IOU Tracker and another well-known variant VIOU Tracker. Our proposal method can also significantly reduce the ID switch value and fragmentation value which are both important metrics in MOT task.
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
Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1806–1819 (2011)
Bergmann, P., Meinhardt, T., Leal-Taixé, L.: Tracking without bells and whistles. In: ICCV, pp. 941–951 (2019)
Bewley, A., Ge, Z., Ott, L., Ramos, F.T., Upcroft, B.: Simple online and realtime tracking. In: ICIP, pp. 3464–3468 (2016)
Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: AVSS, pp. 1–6 (2017)
Bochinski, E., Senst, T., Sikora, T.: Extending IOU based multi-object tracking by visual information. In: AVSS, pp. 1–6 (2018)
Chen, K., et al.: Mmdetection: Open mmlab detection toolbox and benchmark (2019). arXiv\(:\) Computer Vision and Pattern Recognition
Ciaparrone, G., Sánchez, F.L., Tabik, S., Troiano, L., Tagliaferri, R., Herrera, F.: Deep learning in video multi-object tracking: a survey. Neurocomputing 381, 61–88 (2020)
Du, D., et al.: The unmanned aerial vehicle benchmark: object detection and tracking. In: ECCV, pp. 375–391 (2018)
Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., Feng, D.D.: Deep convolutional neural networks for human action recognition using depth maps and postures. IEEE Trans. Syst. Man Cybern. Syst. 49(9), 1806–1819 (2019)
Kim, S.J., Nam, J.Y., Ko, B.C.: Online tracker optimization for multi-pedestrian tracking using a moving vehicle camera. IEEE Access 6, 48675–48687 (2018)
Lin, T., Goyal, P., Girshick, R., He, K., Dollr, P.: Focal loss for dense object detection. In: ICCV, pp. 2999–3007 (2017)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117–2125 (2017)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement (2018). arXiv\(:\) Computer Vision and Pattern Recognition
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 1137–1149 (2015)
Sheng, B., Li, P., Zhang, Y., Mao, L.: Greensea: visual soccer analysis using broad learning system. IEEE Trans. Cybern., 1–15 (2020)
Wang, T., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.: Learning rich features at high-speed for single-shot object detection. In: ICCV, pp. 1971–1980 (2019)
Wang, Z., Zheng, L., Liu, Y., Wang, S.: Towards real-time multi-object tracking. arXiv preprint (2019)
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: ICIP, pp. 3645–3649 (2017)
Zhang, L., Gray, H., Ye, X., Collins, L., Allinson, N.: Automatic individual pig detection and tracking in pig farms. Sensors 19, 1188 (2019)
Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)
Zhang, P., Zheng, L., Jiang, Y., Mao, L., Li, Z., Sheng, B.: Tracking soccer players using spatio-temporal context learning under multiple views. Multimedia Tools Appl. 77(15), 18935–18955 (2017). https://doi.org/10.1007/s11042-017-5316-3
Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection (2019). arXiv\(:\) Computer Vision and Pattern Recognition
Zhao, D., Fu, H., Xiao, L., Wu, T., Dai, B.: Multi-object tracking with correlation filter for autonomous vehicle. Sensors 18, 2004 (2018)
Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q.: Vision meets drones: A challenge. CoRR (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, Z., Qiu, G., Zhang, H., Sheng, B., Li, P. (2020). FIOU Tracker: An Improved Algorithm of IOU Tracker in Video with a Lot of Background Inferences. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2020. Lecture Notes in Computer Science(), vol 12221. Springer, Cham. https://doi.org/10.1007/978-3-030-61864-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-61864-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61863-6
Online ISBN: 978-3-030-61864-3
eBook Packages: Computer ScienceComputer Science (R0)