Online multi-target tracking is an important prerequisite for real-time video sequence analysis. Because of low reliability in target detection, high tracking loss rate and unsmooth trajectory in online multi-target tracking, an online multi-target tracking model based on R-FCN (region based fully convolutional networks) network framework is proposed. Firstly, the target evaluation function based on R-FCN network framework is used to select more reliable candidates in the next frame between KF and detection results. Second, the Siamese network is used to perform similarity measurement based on appearance features to complete the match between candidates and tracks. Finally, the tracking trajectory is optimized by the RANSAC (Random sample consensus) algorithm. In crowded and partially occluded complex scenes, the proposed algorithm has higher target recognition ability, greatly reduces the phenomenon of missed detection and false detection, and the tracking track is more continuous and smooth. The experimental results show that under the same conditions, compared with the existing methods, the performance indicators of the proposed method, such as target tracking accuracy (MOTA), number of lost trajectories (ML) and number of false positives (FN), have been greatly improved.
Multi-candidate association online multi-target tracking based on R-FCN framework
First published at:Jan 14, 2020
 Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 2017, 39(6): 1137–1149.
 Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016: 779–788.
 Liu X, Jin X H. Algorithm for object detection and tracking combined on four inter-frame difference and optical flow methods[J]. Opto-Electronic Engineering, 2018, 45(8): 170665.
刘鑫, 金晅宏. 四帧间差分与光流法结合的目标检测及追踪[J]. 光电工程, 2018, 45(8): 170665.
 Bewley A, Ge Z Y, Ott L, et al. Simple online and realtime tracking[C]//2016 IEEE International Conference on Image Processing (ICIP), Phoenix, 2016: 3464–3468.
 Wojke N, Bewley A, Paulus D. Simple online and realtime tracking with a deep association metric[C]//2017 IEEE International Conference on Image Processing (ICIP), Beijing, 2017: 3645–3649.
 Thoreau M, Kottege N. Improving online multiple object tracking with deep metric learning[Z]. arXiv: 1806.07592v2[cs:CV], 2018.
 Sadeghian A, Alahi A, Savarese S. Tracking the untrackable: Learning to track multiple cues with long-term dependen-cies[Z]. arXiv: 1701.01909[cs:CV], 2017.
 Baisa N L. Online multi-target visual tracking using a HISP filter[C]//13th International Joint Conference on Computer Vi-sion, Imaging and Computer Graphics Theory and Applica-tions, Funchal, 2018.
 Bae S H, Yoon K J. Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 595–610.
 Milan A, Schindler K, Roth S. Multi-target tracking by dis-crete-continuous energy minimization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2054–2068.
 Dehghan A, Assari S M, Shah M. GMMCP tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015: 4091–4099.
 Qi M B, Yue Z L, Shu K, et al. Multi-object tracking using hierarchical data association based on generalized correlation clustering graphs[J]. Acta Automatica Sinica, 2017, 43(1): 152–160.
齐美彬, 岳周龙, 疏坤, 等. 基于广义关联聚类图的分层关联多目标跟踪[J]. 自动化学报, 2017, 43(1): 152–160.
 Wen L Y, Li W B, Yan J J, et al. Multiple target tracking based on undirected hierarchical relation hypergraph[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 1282–1289.
 Zagoruyko S, Komodakis N. Learning to compare image patches via convolutional neural networks[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015: 4353–4361.
 Dai J F, Li Y, He K M, et al. R-FCN: Object detection via re-gion-based fully convolutional networks[Z]. arXiv: 1605.06409[cs:CV], 2016.
 Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and < 0.5 MB model size[Z]. arXiv: 1602.07360[cs:CV], 2016.
 He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[Z]. arXiv: 1406.4729[cs:CV], 2014.
 He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016: 770–778.
 Zheng L, Shen L Y, Tian L, et al. Scalable person re-identification: A benchmark[C]//2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015: 1116–1124.
 Bernardin K, Stiefelhagen R. Evaluating multiple object tracking performance: the CLEAR MOT metrics[J]. EURASIP Journal on Image and Video Processing, 2008, 2008: 246309.
National Natural Science Foundation of China (61673276, 61703277)
Get Citation: E Gui, Wang Yongxiong. Multi-candidate association online multi-target tracking based on R-FCN framework[J]. Opto-Electronic Engineering, 2020, 47(1): 190136.
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