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Multi-object tracking has recently become an important area of computer vision, especially for Advanced Driver Assistance Systems (ADAS).
A survey on visual surveillance of object motion and behaviors
Hu, W., Tan, T., Wang, L., Maybank, S.: · 2004
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., Lecun, Y.: · 2006
Earlier work this paper cites.
Globally-optimal greedy algorithms for tracking a variable number of objects
Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Earlier work this paper cites.
Pedestrian detection: An evaluation of the state of the art
Dollar, P., Wojek, C., Schiele, B., Perona, P.: · 2012
Earlier work this paper cites.
A survey of appearance models in visual object tracking
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A.R., van den Hengel, A.: · 2013
Earlier work this paper cites.
Online object tracking: A benchmark
Wu, Y., Lim, J., Yang, M.H.: · 2013
Earlier work this paper cites.
The way they move: Tracking targets with similar appearance
Dicle, C., Sznaier, M., Camps, O.: · 2013
Earlier work this paper cites.
Microsoft COCO: common objects in context
Lin, T., Maire, M., Belongie, S.J., Bourdev, L.D., Girshick, R.B., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
Cited alongside, same era.
Multiple object tracking: A review
Luo, W., Zhao, X., Kim, T.: · 2014
Cited alongside, same era.
3d traffic scene understanding from movable platforms
Stiller, C., Urtasun, R., Wojek, C., Lauer, M., Geiger, A.: · 2014
Cited alongside, same era.
Continuous energy minimization for multitarget tracking
Milan, A., Roth, S., Schindler, K.: · 2014
Cited alongside, same era.
First step toward model-free, anonymous object tracking with recurrent neural networks
Gan, Q., Guo, Q., Zhang, Z., Cho, K.: · 2015
Cited alongside, same era.
Scalable person re-identification: A benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: · 2015
Later among the works it cites.
Near-online multi-target tracking with aggregated local flow descriptor
Choi, W.: · 2015
Later among the works it cites.
Subgraph decomposition for multi-target tracking
Tang, S., Andres, B., Andriluka, M., Schiele, B.: · 2015
Later among the works it cites.
Multiple hypothesis tracking revisited
Kim, C., Li, F., Ciptadi, A., Rehg, J.M.: · 2015
Later among the works it cites.
Joint probabilistic data association revisited
Rezatofighi, S.H., Milan, A., Zhang, Z., Shi, Q., Dick, A., Reid, I.: · 2015
Later among the works it cites.
Deep tracking: Seeing beyond seeing using recurrent neural networks
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RATM: recurrent attentive tracking model
Kahou, S.E., Michalski, V., Memisevic, R.: · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines vinod nair
Hinton, G.E.:
Cited in the paper.
Multiple object tracking benchmark
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Cited in the paper.
Ondruska, P., Posner, I.: · 2016
Closest in time.
MOT16: A benchmark for multi-object tracking
Milan, A., Leal-Taixé, L., Reid, I.D., Roth, S., Schindler, K.: · 2016
Closest in time.