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Modeling human crowds is an important issue for video surveillance and is a challenging task due to their unpredictable behavior. In this paper, the position of an isolated region that comprises an individual person or a set of occluded persons is detected by background subtraction. Each isolated region is considered a vertex and a human crowd is thus modeled by a graph. To construct a graph, Delaunay triangulation is used to systematically connect vertices and therefore the problem of event detection of human crowds is formulated as measuring the topology variation of consecutive graphs in temporal order. To effectively model the topology variations, local characteristics, such as triangle deformations and eigenvalue-based subgraph analysis, and global features, such as moments, are used and are finally combined as an indicator to detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos. [ABSTRACT FROM PUBLISHER]

Advanced vehicle safety is a critical issue in recent years for automobiles, especially when the number of vehicles is growing rapidly worldwide. The decreasing cost of cameras makes it feasible to have an intelligent system of visually based event detection in front for forward collision avoidance and mitigation. When driving at night, vehicles in front are generally visible by their taillights. The brake lights are particularly important because drivers need to focus on them. Therefore, in this paper, we propose a novel approach that can detect brake lights at night using a camera by analyzing the signal in both spatial and frequency domains. Unlike the traditional approaches that employ the knowledge of the heuristic features, such as symmetry and position of rear facing vehicle, size, and so on, we focus on finding the invariant features from the regions of brake lights in the frequency domain and therefore can conduct the detection process in a part-based manner. Experiments with an extensive dataset show that our proposed system can efficiently and effectively detect brake lights under different lighting and traffic conditions, and thus prove its feasibility in real-world environments. [ABSTRACT FROM PUBLISHER]