A central problem of surveillance is to monitor multiple targets moving in a large-scale, obstacle-ridden environment with occlusions. This paper presents a novel principled Partially Observable Markov Decision Process-based approach to coordinating and controlling a network of active cameras for tracking and observing multiple mobile targets at high resolution in such surveillance environments. Our proposed approach is capable of (a) maintaining a belief over the targets' states (i.e., locations, directions, and velocities) to track them, even when they may not be observed directly by the cameras at all times, (b) coordinating the cameras' actions to simultaneously improve the belief over the targets' states and maximize the expected number of targets observed with a guaranteed resolution, and (c) exploiting the inherent structure of our surveillance problem to improve its scalability (i.e., linear time) in the number of targets to be observed. Quantitative comparisons with state-of-the-art multi-camera coordination and control techniques show that our approach can achieve higher surveillance quality in real time. The practical feasibility of our approach is also demonstrated using real AXIS 214 PTZ cameras
The major drawbacks of these techniques are: (a) They cannot be deployed in real-world surveillance environments with occlusions. In this case, they cannot observe the targets that reside in the occluded regions, hence limiting the active cameras' full surveillance capability. In contrast, our approach does not assume that all targets can be fully observed at every time instance, and hence models a belief of the targets' states to keep track of them when they are not observed by any of the cameras; (b) since the resolution of the wide-view static cameras is low, they often produce inaccurate locations of the targets. This in turn induces errors in targets' directions and velocities which consequently affect the prediction capability of existing surveillance systems. On the other hand, our approach uses only active cameras to observe the targets at high resolution, thus allowing location errors to be kept minimal; and (c) many existing techniques have serious issues of scalability in the number of targets to be observed. Our approach extends our previous work @cite_14 to achieve scalability in partially observable surveillance environments.
@cite_14
2141442361 2161481633 1979838576 2534411223
This paper presents a novel decision-theoretic approach to control and coordinate multiple active cameras for observing a number of moving targets in a surveillance system. This approach offers the advantages of being able to (a) account for the stochasticity of targets' motion via probabilistic modeling, and (b) address the trade-off between maximizing the expected number of observed targets and the resolution of the observed targets through stochastic optimization. One of the key issues faced by existing approaches in multi-camera surveillance is that of scalability with increasing number of targets. We show how its scalability can be improved by exploiting the problem structure: as proven analytically, our decision-theoretic approach incurs time that is linear in the number of targets to be observed during surveillance. As demonstrated empirically through simulations, our proposed approach can achieve high-quality surveillance of up to 50 targets in real time and its surveillance performance degrades gracefully with increasing number of targets. We also demonstrate our proposed approach with real AXIS 214 PTZ cameras in maximizing the number of Lego robots observed at high resolution over a surveyed rectangular area. The results are promising and clearly show the feasibility of our decision-theoretic approach in controlling and coordinating the active cameras in real surveillance system. This paper considers the problem of sustained multicamera tracking in the presence of occlusion and changes in the target motion model. The key insight of the proposed method is the fact that, under mild conditions, the 2D trajectories of the target in the image planes of each of the cameras are constrained to evolve in the same subspace. This observation allows for identifying, at each time instant, a single (piecewise) linear model that explains all the available 2D measurements. In turn, this model can be used in the context of a modified particle filter to predict future target locations. In the case where the target is occluded to some of the cameras, the missing measurements can be estimated using the facts that they must lie both in the subspace spanned by previous measurements and satisfy epipolar constraints. Hence, by exploiting both dynamical and geometrical constraints the proposed method can robustly handle substantial occlusion, without the need for performing 3D reconstruction, calibrated cameras or constraints on sensor separation. The performance of the proposed tracker is illustrated with several challenging examples involving targets that substantially change appearance and motion models while occluded to some of the cameras. We propose a novel approach to understanding activities from their partial observations monitored through multiple non-overlapping cameras separated by unknown time gaps. In our approach, each camera view is first decomposed automatically into regions based on the correlation of object dynamics across different spatial locations in all camera views. A new Cross Canonical Correlation Analysis (xCCA) is then formulated to discover and quantify the time delayed correlations of regional activities observed within and across multiple camera views in a single common reference space. We show that learning the time delayed activity correlations offers important contextual information for (i) spatial and temporal topology inference of a camera network; (ii) robust person re-identification and (iii) global activity interpretation and video temporal segmentation. Crucially, in contrast to conventional methods, our approach does not rely on either intra-camera or inter-camera object tracking; it thus can be applied to low-quality surveillance videos featured with severe inter-object occlusions. The effectiveness and robustness of our approach are demonstrated through experiments on 330 hours of videos captured from 17 cameras installed at two busy underground stations with complex and diverse scenes. In video surveillance scenarios, appearances of both human and their nearby scenes may experience large variations due to scale and view angle changes, partial occlusions, or interactions of a crowd. These challenges may weaken the effectiveness of a dedicated target observation model even based on multiple cues, which demands for an agile framework to adjust target observation models dynamically to maintain their discriminative power. Towards this end, we propose a new adaptive way to integrate multi-cue in tracking multiple human driven by human detections. Given a human detection can be reliably associated with an existing trajectory, we adapt the way how to combine specifically devised models based on different cues in this tracker so as to enhance the discriminative power of the integrated observation model in its local neighborhood. This is achieved by solving a regression problem efficiently. Specifically, we employ 3 observation models for a single person tracker based on color models of part of torso regions, an elliptical head model, and bags of local features, respectively. Extensive experiments on 3 challenging surveillance datasets demonstrate long-term reliable tracking performance of this method.