Abstract
This article presents a new approach to the problem of simultaneous tracking of several people in low-resolution sequences from multiple calibrated cameras. Redundancy among cameras is exploited to generate a discrete 3D colored representation of the scene, being the starting point of the processing chain. We review how the initiation and termination of tracks influences the overall tracker performance, and present a Bayesian approach to efficiently create and destroy tracks. Two Monte Carlo-based schemes adapted to the incoming 3D discrete data are introduced. First, a particle filtering technique is proposed relying on a volume likelihood function taking into account both occupancy and color information. Sparse sampling is presented as an alternative based on a sampling of the surface voxels in order to estimate the centroid of the tracked people. In this case, the likelihood function is based on local neighborhoods computations thus dramatically decreasing the computational load of the algorithm. A discrete 3D re-sampling procedure is introduced to drive these samples along time. Multiple targets are tracked by means of multiple filters, and interaction among them is modeled through a 3D blocking scheme. Tests over CLEAR-annotated database yield quantitative results showing the effectiveness of the proposed algorithms in indoor scenarios, and a fair comparison with other state-of-the-art algorithms is presented. We also consider the real-time performance of the proposed algorithm.
Highlights
Tracking multiple objects and keeping record of their identities along time in a cluttered dynamic scene is a major research topic in computer vision, basically fostered by the number of applications that benefit from the retrieved information
We present our proposal for the Particle filtering (PF) implementation. 3.1.1 Likelihood evaluation Binary and color information contained in zt will be employed to define the likelihood function p zt|xjt relating the observation zt with the human body instance given by particle xjt, 1 ≤ j ≤ NP
5 Results and evaluation In order to assess the performance of the proposed tracking systems, they have been tested on the set of
Summary
Tracking multiple objects and keeping record of their identities along time in a cluttered dynamic scene is a major research topic in computer vision, basically fostered by the number of applications that benefit from the retrieved information. A number of methods for camera-based multi-person 3D tracking have been proposed in the literature [4,5,6,7]. A common goal in these systems is robustness under occlusions created by the multiple objects cluttering the scene when estimating the position of a target. In order to avoid these drawbacks, multi-camera tracking techniques exploit spatial redundancy among different views and provide 3D information at the actual scale of the objects in the real world.
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