Associative affinity network learning for multi-object tracking (original) (raw)
References
Andriyenko A, Roth S, Schindler K, 2011. An analytical formulation of global occlusion reasoning for multi-target tracking. IEEE Int Conf on Computer Vision Workshops, p.1839–1846. https://doi.org/10.1109/ICCVW.2011.6130472
Bergmann P, Meinhardt T, Leal-Taixé L, 2019a. Tracking without bells and whistles. IEEE/CVF Int Conf on Computer Vision, p.941–951. https://doi.org/10.1109/ICCV.2019.00103
Bullinger S, Bodensteiner C, Arens M, 2017. Instance flow based online multiple object tracking. IEEE Int Conf on Image Processing, p.785–789. https://doi.org/10.1109/ICIP.2017.8296388
Chen L, Ai HZ, Zhuang ZJ, et al., 2018. Real-time multiple people tracking with deeply learned candidate selection and person re-identification. IEEE Int Conf on Multimedia and Expo, p.1–6. https://doi.org/10.1109/ICME.2018.8486597
Chen S, Gong C, Yang J, et al., 2018. Adversarial metric learning. Proc 27th Int Joint Conf on Artificial Intelligence, p.2021–2027. https://doi.org/10.24963/IJCAI.2018/279
Chen S, Luo L, Yang J, et al., 2019. Curvilinear distance metric learning. Proc 33rd Int Conf on Neural Information Processing Systems, p.4223–4232.
Choi W, 2015. Near-online multi-target tracking with aggregated local flow descriptor. IEEE Int Conf on Computer Vision, p.3029–3037. https://doi.org/10.1109/ICCV.2015.347
Chu P, Ling HB, 2019. FAMNet: joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. IEEE/CVF Int Conf on Computer Vision, p.6171–6180. https://doi.org/10.1109/ICCV.2019.00627
Chu Q, Ouyang WL, Li HS, et al., 2017. Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism. Proc IEEE Int Conf on Computer Vision, p.4846–4855. https://doi.org/10.1109/ICCV.2017.518
Dalal N, Triggs B, 2005. Histograms of oriented gradients for human detection. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.886–893. https://doi.org/10.1109/CVPR.2005.177
Fagot-Bouquet L, Audigier R, Dhome Y, et al., 2016. Improving multi-frame data association with sparse representations for robust near-online multi-object tracking. Proc 14th European Conf on Computer Vision, p.774–790. https://doi.org/10.1007/978-3-319-46484-8_47
Fang K, Xiang Y, Li XC, et al., 2018. Recurrent autoregressive networks for online multi-object tracking. IEEE Winter Conf on Applications of Computer Vision, p.466–475. https://doi.org/10.1109/WACV.2018.00057
Feichtenhofer C, Pinz A, Zisserman A, 2017. Detect to track and track to detect. IEEE Int Conf on Computer Vision, p.3057–3065. https://doi.org/10.1109/ICCV.2017.330
Han XF, Leung T, Jia YG, et al., 2015. MatchNet: unifying feature and metric learning for patch-based matching. IEEE Conf on Computer Vision and Pattern Recognition, p.3279–3286. https://doi.org/10.1109/CVPR.2015.7298948
Henschel R, Leal-Taixé L, Cremers D, et al., 2018. Fusion of head and full-body detectors for multi-object tracking. IEEE/CVF Conf on Computer Vision and Pattern Recognition Workshops, p.1509–1518. https://doi.org/10.1109/CVPRW.2018.00192
Hermans A, Beyer L, Leibe B, 2017. In defense of the triplet loss for person re-identification. https://arxiv.org/abs/1703.07737
Ilg E, Mayer N, Saikia T, et al., 2017. FlowNet 2.0: evolution of optical flow estimation with deep networks. IEEE Conf on Computer Vision and Pattern Recognition, p.1647–1655. https://doi.org/10.1109/CVPR.2017.179
Keuper M, Tang SY, Yu ZJ, et al., 2016. A multi-cut formulation for joint segmentation and tracking of multiple objects. https://arxiv.org/abs/1607.06317
Kim C, Li FX, Ciptadi A, et al., 2015. Multiple hypothesis tracking revisited. IEEE Int Conf on Computer Vision, p.4696–4704. https://doi.org/10.1109/ICCV.2015.533
Lan L, Tao DC, Gong C, et al., 2016. Online multi-object tracking by quadratic pseudo-Boolean optimization. Proc 25th Int Joint Conf on Artificial Intelligence, p.3396–3402.
Leal-Taixé L, Canton-Ferrer C, Schindler K, 2016. Learning by tracking: Siamese CNN for robust target association. IEEE Conf on Computer Vision and Pattern Recognition Workshops, p.418–425. https://doi.org/10.1109/CVPRW.2016.59
Ma C, Yang CS, Yang F, et al., 2018. Trajectory factory: tracklet cleaving and re-connection by deep Siamese Bi-GRU for multiple object tracking. IEEE Int Conf on Multimedia and Expo, p.1–6. https://doi.org/10.1109/ICME.2018.8486454
Maksai A, Wang XC, Fleuret F, et al., 2017. Non-Markovian globally consistent multi-object tracking. IEEE Int Conf on Computer Vision, p.2563–2573. https://doi.org/10.1109/ICCV.2017.278
Milan A, Rezatofighi SH, Garg R, et al., 2017a. Data-driven approximations to NP-hard problems. Proc 31st AAAI Conf on Artificial Intelligence, p.1453–1459.
Milan A, Rezatofighi SH, Dick A, et al., 2017b. Online multi-target tracking using recurrent neural networks. Proc 31st AAAI Conf on Artificial Intelligence, p.4225–4232.
Rezatofighi SH, Milan A, Zhang Z, et al., 2015. Joint probabilistic data association revisited. IEEE Int Conf on Computer Vision, p.3047–3055. https://doi.org/10.1109/ICCV.2015.349
Ristani E, Tomasi C, 2018. Features for multi-target multi-camera tracking and re-identification. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.6036–6046. https://doi.org/10.1109/CVPR.2018.00632
Ristani E, Solera F, Zou R, et al., 2016. Performance measures and a data set for multi-target, multi-camera tracking. European Conf on Computer Vision, p.17–35. https://doi.org/10.1007/978-3-319-48881-3_2
Sadeghian A, Alahi A, Savarese S, 2017. Tracking the untrackable: learning to track multiple cues with long-term dependencies. IEEE Int Conf on Computer Vision, p.300–311. https://doi.org/10.1109/ICCV.2017.41
Schulter S, Vernaza P, Choi W, et al., 2017. Deep network flow for multi-object tracking. IEEE Conf on Computer Vision and Pattern Recognition, p.2730–2739. https://doi.org/10.1109/CVPR.2017.292
Shen H, Huang LC, Huang C, et al., 2018. Tracklet association tracker: an end-to-end learning-based association approach for multi-object tracking. https://arxiv.org/abs/1808.01562
Shrivastava A, Gupta A, Girshick R, 2016. Training region-based object detectors with online hard example mining. IEEE Conf on Computer Vision and Pattern Recognition, p.761–769. https://doi.org/10.1109/CVPR.2016.89
Son J, Baek M, Cho M, et al., 2017. Multi-object tracking with quadruplet convolutional neural networks. IEEE Conf on Computer Vision and Pattern Recognition, p.3786–3795. https://doi.org/10.1109/CVPR.2017.403
Tang SY, Andriluka M, Andres B, et al., 2017. Multiple people tracking by lifted multicut and person reidentification. IEEE Conf on Computer Vision and Pattern Recognition, p.3701–3710. https://doi.org/10.1109/CVPR.2017.394
Wang B, Wang L, Shuai B, et al., 2016. Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association. IEEE Conf on Computer Vision and Pattern Recognition Workshops, p.386–393. https://doi.org/10.1109/CVPRW.2016.55
Wang XY, Han TX, Yan S, 2009. An HOG-LBP human detector with partial occlusion handling. Proc IEEE 12th Int Conf on Computer Vision, p.32–39. https://doi.org/10.1109/ICCV.2009.5459207
Wojke N, Bewley A, Paulus D, 2017. Simple online and realtime tracking with a deep association metric. IEEE Int Conf on Image Processing, p.3645–3649. https://doi.org/10.1109/ICIP.2017.8296962
Xiang Y, Alahi A, Savarese S, 2015. Learning to track: online multi-object tracking by decision making. IEEE Int Conf on Computer Vision, p.4705–4713. https://doi.org/10.1109/ICCV.2015.534
Yang F, Choi W, Lin YQ, 2016. Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. IEEE Conf on Computer Vision and Pattern Recognition, p.2129–2137. https://doi.org/10.1109/CVPR.2016.234
Yin JB, Wang WG, Meng QH, et al., 2020. A unified object motion and affinity model for online multi-object tracking. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.6767–6776. https://doi.org/10.1109/CVPR42600.2020.00680