Tracking Algorithm Based on Video Person Reidentification and Spatiotemporal Feature Fusion

Guancheng Hui; Kaifang Li; Ming Xin; Miaohui Zhang

doi:10.3788/LOP202259.1215004

[1] 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, 40, 595-610(2018).

[2] Fagot-Bouquet L, Audigier R, Dhome Y et al. Improving multi-frame data association with sparse representations for robust near-online multi-object tracking[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016, 9912, 774-790(2016).

[3] Wang Z D, Zheng L, Liu Y X et al. Towards real-time multi-object tracking[EB/OL]. https://arxiv.org/abs/1909.12605

[4] Li C, Yang D D, Song P et al. Global-aware siamese network for thermal infrared object tracking[J]. Acta Optica Sinica, 41, 0615002(2021).

[5] Li F J, Liu H H, Ren H G et al. Scale adaptive kernel correlation tracking method with high confidence[J]. Laser & Optoelectronics Progress, 58, 0815004(2021).

[6] Kim C, Li F X, Ciptadi A et al. Multiple hypothesis tracking revisited[C], 4696-4704(2015).

[7] Tang S Y, Andriluka M, Andres B et al. Multiple people tracking by lifted multicut and person re-identification[C], 3701-3710(2017).

[8] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[9] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points[EB/OL]. https://arxiv.org/abs/1904.07850

[10] Liu K W, Fang P P, Xiong H X et al. Person re-identification based on multi-layer feature[J]. Laser & Optoelectronics Progress, 57, 081503(2020).

[11] Luo H, Jiang W, Fan X et al. A survey on deep learning based person re-identification[J]. Acta Automatica Sinica, 45, 2032-2049(2019).

[12] Cipolla R, Gal Y, Kendall A. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[C], 7482-7491(2018).

[13] Kuhn H W. The Hungarian method for the assignment problem[J]. Naval Research Logistics Quarterly, 2, 83-97(1955).

[14] Welch G, Bishop G. An introduction to the Kalman filter[EB/OL]. https://is.muni.cz/el/1431/podzim2014/Bi0440/um/kalman_intro.pdf?lang=cs

[15] Bolme D S, Beveridge J R, Draper B A et al. Visual object tracking using adaptive correlation filters[C], 2544-2550(2010).

[16] Henriques J F, Caseiro R, Martins P et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 583-596(2015).

[17] Dollar P, Wojek C, Schiele B et al. Pedestrian detection: a benchmark[C], 304-311(2009).

[18] Milan A, Leal-Taixé L, Reid I et al. MOT16: a benchmark for multi-object tracking[EB/OL]. https://arxiv.org/abs/1603.00831

[19] Xiao T, Li S, Wang B C et al. Joint detection and identification feature learning for person search[C], 3376-3385(2017).

[20] Zheng L, Zhang H H, Sun S Y et al. Person re-identification in the wild[C], 3346-3355(2017).

[21] Dendorfer P, Rezatofighi H, Milan A et al. MOT20: a benchmark for multi object tracking in crowded scenes[EB/OL]. https://arxiv.org/abs/2003.09003

[22] Bewley A, Ge Z Y, Ott L et al. Simple online and realtime tracking[EB/OL]. https://arxiv.org/abs/1602.00763

[23] Sanchez-Matilla R, Poiesi F, Cavallaro A. Online multi-target tracking with strong and weak detections[M]. Hua G, Jégou H. Computer vision-ECCV 2016 workshops, 9914, 84-99(2016).

[24] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014, 8693, 740-755(2014).

[25] Wojke N, Bewley A, Paulus D. Simple online and realtime tracking with a deep association metric[C], 3645-3649(2017).

[26] Fang K, Xiang Y, Li X C et al. Recurrent autoregressive networks for online multi-object tracking[C], 466-475(2018).

[27] Wan X Y, Wang J J, Kong Z F et al. Multi-object tracking using online metric learning with long short-term memory[C], 788-792(2018).

[28] Pang B, Li Y Z, Zhang Y F et al. TubeTK: adopting tubes to track multi-object in a one-step training model[C], 6307-6317(2020).

[29] Zhou Z W, Xing J L, Zhang M D et al. Online multi-target tracking with tensor-based high-order graph matching[C], 1809-1814(2018).

[30] Mahmoudi N, Ahadi S M, Rahmati M. Multi-target tracking using CNN-based features: CNNMTT[J]. Multimedia Tools and Applications, 78, 7077-7096(2019).

[31] Yu F W, Li W B, Li Q Q et al. POI: multiple object tracking with high performance detection and appearance feature[M]. Hua G, Jégou H. Computer vision-ECCV 2016 workshops, 9914, 36-42(2016).

[32] Peng J L, Wang C G, Wan F B et al. Chained-tracker: chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020, 12349, 145-161(2020).

[33] Sun S J, Akhtar N, Song H S et al. Deep affinity network for multiple object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 104-119(2021).

[34] Zhou X Y, Koltun V, Krähenbühl P. Tracking objects as points[EB/OL]. https://arxiv.org/abs/2004.01177

[35] Saleh F, Aliakbarian S, Rezatofighi H et al. Probabilistic tracklet scoring and inpainting for multiple object tracking[C], 14324-14334(2021).

[36] Brasó G, Leal-Taixé L. Learning a neural solver for multiple object tracking[C], 6246-6256(2020).