Robust 3D multi-camera tracking from 2D mono-camera tracks by bayesian association

Visual tracking of people is essential automatic scene understanding and surveillance of areas of interest. Monocular 2D tracking has been largely studied, but it usually provides inadequate information for event nterpretation, and also proves insufficiently robust, due to view-point limitations (occlusions, etc.). In this paper, we present a light but automatic and robust 3D tracking method using multiple calibrated cameras. It is based on off-the-shelf 2D tracking systems running independently in each camera of the system, combined using Bayesian association of the monocular tracks. The proposed system shows excellent results even in challenging situations, proving itself able to automatically boost and recover from possible errors

Robust multi-camera tracking from schematic descriptions

Although monocular 2D tracking has been largely studied in the literature, it suffers from some inherent problems, mainly when handling persistent occlusions, that limit its performance in practical situations. Tracking methods combining observations from multiple cameras seem to solve these problems. However, most multi-camera systems require detailed information from each view, making it impossible their use in real networks with low transmission rate. In this paper, we present a robust multi-camera 3D tracking method which works on schematic descriptions of the observations performed by each camera of the system, allowing thus its performance in real surveillance networks. It is based on unspecific 2D detection systems working independently in each camera, whose results are smartly combined by means of a Bayesian association method based on geometry and color, allowing the 3D tracking of the objects of the scene with a Particle Filter. The tests performed show the excellent performance of the system, even correcting possible failures of the 2D processing modules

Geometry-Based Multiple Camera Head Detection in Dense Crowds

This paper addresses the problem of head detection in crowded environments. Our detection is based entirely on the geometric consistency across cameras with overlapping fields of view, and no additional learning process is required. We propose a fully unsupervised method for inferring scene and camera geometry, in contrast to existing algorithms which require specific calibration procedures. Moreover, we avoid relying on the presence of body parts other than heads or on background subtraction, which have limited effectiveness under heavy clutter. We cast the head detection problem as a stereo MRF-based optimization of a dense pedestrian height map, and we introduce a constraint which aligns the height gradient according to the vertical vanishing point direction. We validate the method in an outdoor setting with varying pedestrian density levels. With only three views, our approach is able to detect simultaneously tens of heavily occluded pedestrians across a large, homogeneous area.Comment: Proceedings of the 28th British Machine Vision Conference (BMVC) - 5th Activity Monitoring by Multiple Distributed Sensing Workshop, 201

Unsupervised Camera Localization in Crowded Spaces

Existing camera networks in public spaces such as train terminals or malls can help social robots to navigate crowded scenes. However, the localization of the cameras is required, i.e., the positions and poses of all cameras in a unique reference. In this work, we estimate the relative location of any pair of cameras by solely using noisy trajectories observed from each camera. We propose a fully unsupervised learningtechniqueusingunlabelledpedestriansmotionpatterns captured in crowded scenes. We first estimate the pairwise camera parameters by optimally matching single-view pedestrian tracks using social awareness. Then, we show the impact of jointly estimating the network parameters. This is done by formulating a nonlinear least square optimization problem, leveraging a continuous approximation of the matching function. We evaluate our approach in real-world environments such as train terminals, whereseveralhundredsofindividualsneedtobetrackedacross dozens of cameras every second