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Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution.
The hungarian method for the assignment problem
Kuhn, H.W.: · 1955
Earlier work this paper cites.
Evaluating multiple object tracking performance: the clear mot metrics
Bernardin, K., Stiefelhagen, R.: · 2008
Earlier work this paper cites.
A mobile vision system for robust multi-person tracking
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: · 2008
Earlier work this paper cites.
Robust tracking-by-detection using a detector confidence particle filter
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: · 2009
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Pedestrian detection: A benchmark
Dollár, P., Wojek, C., Schiele, B., Perona, P.: · 2009
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Object detection with discriminatively trained part-based models
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: · 2010
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Near-online multi-target tracking with aggregated local flow descriptor
Choi, W.: · 2015
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Poi: multiple object tracking with high performance detection and appearance feature
Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., Yan, J.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
Earlier work this paper cites.
Mot16: A benchmark for multi-object tracking
Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: · 2016
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Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers
Yang, F., Choi, W., Lin, Y.: · 2016
Cited alongside, same era.
Multi-class multi-object tracking using changing point detection
Lee, B., Erdenee, E., Jin, S., Nam, M.Y., Jung, Y.G., Rhee, P.K.: · 2016
Cited alongside, same era.
Online multi-target tracking with strong and weak detections
Sanchez-Matilla, R., Poiesi, F., Cavallaro, A.: · 2016
Cited alongside, same era.
High-speed tracking-by-detection without using image information
Bochinski, E., Eiselein, V., Sikora, T.: · 2017
Cited alongside, same era.
Enhancing detection model for multiple hypothesis tracking
Chen, J., Sheng, H., Zhang, Y., Xiong, Z.: · 2017
Cited alongside, same era.
Online multi-object tracking using cnn-based single object tracker with spatial-temporal attention mechanism
Online multi-object tracking with dual matching attention networks
Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., Yang, M.H.: · 2018
Later among the works it cites.
Osmo: Online specific models for occlusion in multiple object tracking under surveillance scene
Gao, X., Jiang, T.: · 2018
Later among the works it cites.
Multi-object tracking with neural gating using bilinear lstm
Kim, C., Li, F., Rehg, J.M.: · 2018
Later among the works it cites.
Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking
Bae, S.H., Yoon, K.J.: · 2018
Later among the works it cites.
Real-time multiple people tracking with deeply learned candidate selection and person re-identification
Chen, L., Ai, H., Zhuang, Z., Shang, C.: · 2018
Later among the works it cites.
Motion segmentation & multiple object tracking by correlation co-clustering
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Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., Yu, N.: · 2017
Cited alongside, same era.
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., Paulus, D.: · 2017
Cited alongside, same era.
Yolo9000: better, faster, stronger
Redmon, J., Farhadi, A.: · 2017
Cited alongside, same era.
Soft-nms – improving object detection with one line of code
Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: · 2017
Cited alongside, same era.
Multi-object tracking with quadruplet convolutional neural networks
Son, J., Baek, M., Cho, M., Han, B.: · 2017
Cited alongside, same era.
Multiple people tracking by lifted multicut and person re-identification
Tang, S., Andriluka, M., Andres, B., Schiele, B.: · 2017
Cited alongside, same era.
Keuper, M., Tang, S., Andres, B., Brox, T., Schiele, B.: · 2018
Later among the works it cites.
Tracking without bells and whistles
Bergmann, P., Meinhardt, T., Leal-Taixe, L.: · 2019
Later among the works it cites.
Deep affinity network for multiple object tracking
Sun, S., Akhtar, N., Song, H., Mian, A.S., Shah, M.: · 2019
Later among the works it cites.
Famnet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking
Chu, P., Ling, H.: · 2019
Later among the works it cites.
Tracknet: Simultaneous object detection and tracking and its application in traffic video analysis
Li, C., Dobler, G., Feng, X., Wang, Y.: · 2019
Later among the works it cites.
Multi-target tracking using cnn-based features: Cnnmtt
Mahmoudi, N., Ahadi, S.M., Rahmati, M.: · 2019
Later among the works it cites.
Retinatrack: Online single stage joint detection and tracking
Lu, Z., Rathod, V., Votel, R., Huang, J.: · 2020
Closest in time.
Tpm: Multiple object tracking with tracklet-plane matching
Peng, J., Wang, T., Lin, W., Wang, J., See, J., Wen, S., Ding, E.: · 2020
Closest in time.