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This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework.
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Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. TPAMI 33 (2011) 1806-1819
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Sakaino, H.: Video-based tracking, learning, and recognition method for multiple moving objects. IEEE trans. on circuits and systems for video technology 23 (2013) 1661-1674
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Choi, W., Pantofaru, C., Savarese, S.: A general framework for tracking multiple people from a moving camera. TPAMI 35 (2013) 1577-1591
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Wang, C., Liu, H., Gao, Y.: Scene-Adaptive Hierarchical Data Association for Multiple Objects Tracking. IEEE Signal Processing Letters 21 (2014) 697-701
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Hamid Rezatofighi, S., Milan, A., Zhang, Z., Shi, Q., Dick, A., Reid, I.: Joint probabilistic data association revisited. In: ICCV. (2015)
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Xiao, J., Oussalah, M.: Collaborative Tracking for Multiple Objects in the Presence of Inter-Occlusions. IEEE Trans. on Circuits and Systems for Video Technology 26 (2016) 304-318
2016
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2016
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2016
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2016
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Wang, X., Turetken, E., Fleuret, F., Fua, P.: Tracking Interacting Objects Using Intertwined Flows. TPAMI 99 (2016) 1-1
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Lee, B., Erdenee, E., Jin, S., Rhee, P. K.: Efficient object detection using convolutional neural network-based hierarchical feature modeling. Signal, Image and Video Processing (2016)
2016
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