method is proposed for identifying five crowd behaviors (bottlenecks, fountainheads, lanes, arches, and blocking) in visual scenes. In the algorithm, a scene is overlaid by a grid of particles initializing a dynamical system defined by the optical flow. Time integration of the dynamical system provides particle trajectories that represent the motion in the scene; these trajectories are used to locate regions of interest in the scene. Linear approximation of the dynamical system provides behavior classification through the Jacobian matrix; the eigenvalues determine the dynamic stability of points in the flow and each type of stability corresponds to one of the five crowd behaviors. The eigenvalues are only considered in the regions of interest, consistent with the linear approximation and the implicated behaviors. The algorithm is repeated over sequential clips of a video in order to record changes in eigenvalues, which may imply changes in behavior. The method was tested on over 60 crowd and traffic videos.

Computing descriptors for videos is a crucial task in computer vision. In this paper, we propose a global video descriptor for classification of videos. Our method, bypasses the detection of interest points, the extraction of local video descriptors and the quantization of descriptors into a code book; it represents each video sequence as a single feature vector. Our global descriptor is computed by applying a bank of 3-D spatiotemporal filters on the frequency spectrum of a video sequence; hence it integrates the information about the motion and scene structure. We tested our approach on three datasets, KTH[1], UCF50 [2] and HMDB51 [3], and obtained promising results which demonstrate the robustness and the discriminative power of our global video descriptor for classifying videos of various actions. In addition, the combination of our global descriptor and a local descriptor resulted in the highest classification accuracies on UCF50 and HMDB51 datasets.

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