Abstract. The assignment of multiple person tracks to a set of candidate person locations in overlapping camera views is potentially computationaly intractable, as observables might depend upon visibility order, and thus upon the decision which of the candidate locations represent actual persons and which do not. In this paper, we present an approximate assignment method which consists of two stages. In a hypothesis generation stage, the similarity between track and measurement is based on a subset of observables (appearance, motion) that is independent of the classification of candidate locations. This allows the computation of the K-best assignment in low polynomial time by standard graph matching methods. In a subsequent hypothesis verification stage, the known person positions associated with the K-best solutions are used to define the full set of observables, which are used to compute the maximum likelihood assignment. We demonstrate that our method outperforms the state-of-the-art on a complex outdoor dataset.
This paper presents a multi-camera system to track multiple persons in complex, dynamic environments. Position measurements are obtained by carving out the space defined by foreground regions in the overlapping camera views and projecting these onto blobs on the ground plane. Person appearance is described in terms of the colour histograms in the various camera views of three vertical body regions (head-shoulder, torso, legs). The assignment of measurements to tracks (modelled by Kalman filters) is done in a non-greedy, global fashion based on ground plane position and colour appearance. The advantage of the proposed approach is that the decision on correspondences across cameras is delayed until it can be performed at the object-level, where it is more robust.We demonstrate the effectiveness of the proposed approach using data from three cameras overlooking a complex outdoor setting (train platform), containing a significant amount of lighting and background changes.
Abstract. We present a comparative study for tracking multiple persons using cameras with overlapping views. The evaluated methods consist of two batch mode trackers (Berclaz et al, 2011, Ben-Shitrit et al, 2011 and one recursive tracker (Liem and Gavrila, 2011), which integrate appearance cues and temporal information differently. We also added our own improved version of the recursive tracker. Furthermore, we investigate the effect of the type of background estimation (static vs. adaptive) on tracking performance. Experiments are performed on two novel and challenging multi-person surveillance data sets (indoor, outdoor), made public to facilitate benchmarking. We show that our adaptation of the recursive method outperforms the other stand-alone trackers.
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