Wide-area monitoring for a smart community can be challenging in systems engineering because of its large scale and heterogeneity at the sensor, algorithm, and visualization levels. A smart interface to visualize high-level information fused from a diversity of low-level surveillance data, and to facilitate rapid response of events, is critical for the design of the system. This paper presents an event-driven visualization mechanism fusing multimodal information for a large-scale intelligent video surveillance system. The mechanism proactively helps security personnel intuitively be aware of events through close cooperation among visualization, data fusion, and sensor tasking. The visualization not only displays 2-D, 3-D, and geographical information within a condensed form of interface but also automatically shows the only important video streams corresponding to spontaneous alerts and events by a decision process called display switching arbitration. The display switching arbitration decides the importance of cameras by score ranking that considers event urgency and semantic object features. This system has been successfully deployed in a campus to demonstrate its usability and efficiency for an installation with two camera clusters that include dozens of cameras, and with a lot of video analytics to detect alerts and events. A further simulation comparing the display switching arbitration with similar camera selection methods shows that our method improves the visualization by selecting better representative camera views and reducing redundant switchover among multiview videos.

Detection of camera anomaly and tampering have attracted increasing interest in video surveillance for real-time alert of camera malfunction. However, the anomaly detection for traffic cameras monitoring vehicles and recognizing license plates has not been formally studied and it cannot be solved by existing methods. In this paper, we propose a camera anomaly detection method for traffic scene that has distinct characteristics of dynamics due to traffic flow and traffic crowd, compared with normal surveillance scene. Image quality used as low-level features are measured by no-referenced metrics. Image dynamics used as mid-level features are computed by histogram distribution of optical flow. A two-stage classifier for the detection of anomaly is devised by the modeling of image quality and video dynamics with probabilistic state transition. The proposed approach is robust to many challenging issues in urban surveillance scenarios and has very low false alarm rate. Experiments are conducted on real-world videos recorded in traffic scene including the situations of high traffic flow and severe crowding. Our test results demonstrate that the proposed method is superior to previous methods on both precision rate and false alarm rate for the anomaly detection of traffic cameras.

Restoration of fog images is important for the deweathering issue in computer vision. The problem is ill-posed and can be regularized within a Bayesian context using a probabilistic fusion model. This paper presents a multiscale depth fusion (MDF) method for defog from a single image. A linear model representing the stochastic residual of nonlinear filtering is first proposed. Multiscale filtering results are probabilistically blended into a fused depth map based on the model. The fusion is formulated as an energy minimization problem that incorporates spatial Markov dependence. An inhomogeneous Laplacian-Markov random field for the multiscale fusion regularized with smoothing and edge-preserving constraints is developed. A nonconvex potential, adaptive truncated Laplacian, is devised to account for spatially variant characteristics such as edge and depth discontinuity. Defog is solved by an alternate optimization algorithm searching for solutions of depth map by minimizing the nonconvex potential in the random field. The MDF method is experimentally verified by real-world fog images including cluttered-depth scene that is challenging for defogging at finer details. The fog-free images are restored with improving contrast and vivid colors but without over-saturation. Quantitative assessment of image quality is applied to compare various defog methods. Experimental results demonstrate that the accurate estimation of depth map by the proposed edge-preserved multiscale fusion should recover high-quality images with sharp details.