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This paper presents a new approach to real-time human detection and tracking in cluttered and dynamic environments by integration of RGB and depth data. We introduce the notion of Point Ensemble Image, which fully encodes both RGB and depth information from a virtual plan-view perspective, and we reveal that human detection and tracking in 3D space can be performed very effectively based on this new representation. Our human detector is able to take advantage of depth data by effectively locate physically plausible candidates as a first step, and then both depth and color information is made full use of in a supervised learning manner at the second stage. 3D trajectories of humans are finally generated by data association in which joint statistics of color and height are computed and compared. Experimental results show that the system is able to work satisfactorily in complex real-world situations.
Liu et al. (Sun,) studied this question.