Multi-candidate association online multi-target tracking based on R-FCN framework

E Gui; Wang Yongxiong

doi:10.12086/oee.2020.190136

[1] 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.

[2] 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 Ve-gas, 2016: 779–788.

[4] 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.

[5] Wojke N, Bewley A, Paulus D. Simple online and realtime tracking with a deep association metric[C]//2017 IEEE Interna-tional Conference on Image Processing (ICIP), Beijing, 2017: 3645–3649.

[6] Thoreau M, Kottege N. Improving online multiple object tracking with deep metric learning[Z]. arXiv: 1806.07592v2[cs:CV], 2018.

[7] Sadeghian A, Alahi A, Savarese S. Tracking the untrackable: Learning to track multiple cues with long-term dependencies[Z]. arXiv: 1701.01909[cs:CV], 2017.

[8] 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 Applications, Funchal, 2018.

[9] Bae S H, Yoon K J. Confidence-based data association and discriminative deep appearance learning for robust online mul-ti-object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 595–610.

[10] 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.

[11] 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.

[13] 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, Co-lumbus, 2014: 1282–1289.

[14] Zagoruyko S, Komodakis N. Learning to compare image patches via convolutional neural networks[C]//2015 IEEE Con-ference on Computer Vision and Pattern Recognition (CVPR), Boston, 2015: 4353–4361.

[15] 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.

[16] Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: Alex-net-level accuracy with 50x fewer parameters and < 0.5 MB model size[Z]. arXiv: 1602.07360[cs:CV], 2016.

[17] 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.

[18] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vi-sion and Pattern Recognition (CVPR), Las Vegas, 2016: 770–778.

[19] 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.

[20] Bernardin K, Stiefelhagen R. Evaluating multiple object tracking performance: the CLEAR MOT metrics[J]. EURASIP Journal on Image and Video Processing, 2008, 2008: 246309.