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Multi-Object Tracking (MOT) is a challenging task in the complex scene such as surveillance and autonomous driving.
“Evaluating multiple object tracking performance: the clear mot metrics,”
K. Bernardin and R. Stiefelhagen, · 2008
Earlier work this paper cites.
“The way they move: Tracking multiple targets with similar appearance,”
C. Dicle, O. I. Camps, and M. Sznaier, · 2013
Earlier work this paper cites.
“Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning,”
S.-H. Bae and K.-J. Yoon, · 2014
Earlier work this paper cites.
“Learning to track: Online multi-object tracking by decision making,”
Y. Xiang, A. Alahi, and S. Savarese, · 2015
Earlier work this paper cites.
“Near-online multi-target tracking with aggregated local flow descriptor,”
W. Choi, · 2015
Earlier work this paper cites.
“Multiple hypothesis tracking revisited,”
C. Kim, F. Li, A. Ciptadi, and J. M. Rehg, · 2015
Earlier work this paper cites.
“Scalable person re-identification: A benchmark,”
L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, · 2015
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
D. Kingma and J. Ba, · 2015
Earlier work this paper cites.
“MOT16: A benchmark for multi-object tracking,”
A. Milan, L. Leal-Taixé, I. D. Reid, S. Roth, and K. Schindler, · 2016
Earlier work this paper cites.
“Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association,”
B. Wang, L. Wang, B. Shuai, Z. Zuo, T. Liu, K. Luk Chan, and G. Wang, · 2016
Earlier work this paper cites.
“Long-term time-sensitive costs for crf-based tracking by detection,”
N. Le, A. Heili, and J.-M. Odobez, · 2016
Cited alongside, same era.
“Online multi-object tracking via structural constraint event aggregation,”
J. Hong Yoon, C.-R. Lee, M.-H. Yang, and K.-J. Yoon, · 2016
Cited alongside, same era.
“Temporal dynamic appearance modeling for online multi-person tracking,”
M. Yang and Y. Jia, · 2016
Cited alongside, same era.
“Social lstm: Human trajectory prediction in crowded spaces,”
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, · 2016
Cited alongside, same era.
“Recurrent convolutional network for video-based person re-identification,”
N. McLaughlin, J. Martinez del Rincon, and P. Miller, · 2016
Cited alongside, same era.
“Wide residual networks,”
S. Zagoruyko and N. Komodakis, · 2016
Cited alongside, same era.
“Online multi-object tracking using cnn-based single object tracker with spatial-temporal attention mechanism,”
Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, and N. Yu, · 2017
Later among the works it cites.
“Tracking the untrackable: Learning to track multiple cues with long-term dependencies,”
A. Sadeghian, A. Alahi, and S. Savarese, · 2017
Later among the works it cites.
“Enhancing detection model for multiple hypothesis tracking,”
J. Chen, H. Sheng, Y. Zhang, and Z. Xiong, · 2017
Later among the works it cites.
“Deep network flow for multi-object tracking,”
S. Schulter, P. Vernaza, W. Choi, and M. Chandraker, · 2017
Later among the works it cites.
“Joint graph decomposition and node labeling: Problem, algorithms, applications,”
E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele, and B. Andres, · 2017
Later among the works it cites.
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“Mars: A video benchmark for large-scale person re-identification,”
L. Zheng, Z. Bie, Y. Sun, J. Wang, C. Su, S. Wang, and Q. Tian, · 2016
Cited alongside, same era.
“Performance measures and a data set for multi-target, multi-camera tracking,”
E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi, · 2016
Cited alongside, same era.
“Multiple people tracking by lifted multicut and person reidentification,”
S. Tang, M. Andriluka, B. Andres, and B. Schiele, · 2017
Cited alongside, same era.
“Globally consistent multi-people tracking using motion patterns,”
A. Maksai, X. Wang, F. Fleuret, and P. Fua,
Cited in the paper.
R. Henschel, L. Leal-Taix¨¦, D. Cremers, and B. Rosenhahn, · 2017
Later among the works it cites.
“Multi-object tracking with quadruplet convolutional neural networks,”
J. Son, M. Baek, M. Cho, and B. Han, · 2017
Later among the works it cites.
“Kalman filter and iterative-hungarian algorithm implementation for low complexity point tracking as part of fast multiple object tracking system,”
B. Sahbani and W. Adiprawita, · 2017
Later among the works it cites.
“Pathtrack: Fast trajectory annotation with path supervision,”
S. Manen, M. Gygli, D. Dai, and L. Van Gool, · 2017
Later among the works it cites.