We introduce a novel method for abnormal crowd event detection in surveillance videos. Particularly, our work focuses on panic and escape behavior detection that may appear because of violent events and natural disasters. First, optical flow vectors are computed to generate a motion information image (MII) for each frame, and then MIIs are used to train a convolutional neural network (CNN) for abnormal crowd event detection. The proposed MII is a new formulation that provides a visual appearance of crowd motion. The proposed MIIs make the discrimination between normal and abnormal behaviors easier. The MII is mainly based on the optical flow magnitude, and angle difference computed between the optical flow vectors in consecutive frames. A CNN is employed to learn normal and abnormal crowd behaviors using MIIs. The MII generation, and the combination with a CNN is a new approach in the context of abnormal crowd behavior detection. Experiments are performed on commonly used datasets such as UMN and PETS2009. Evaluation indicates that our method achieves the best results. INDEX TERMS Crowd behavior analysis, anomaly detection, motion information image, convolutional neural network.
We introduce a novel approach for team activity recognition in sports. Given the positions of team players from a plan view of the playing field at any given time, we solve a particular Poisson equation to generate a smooth distribution defined on whole playground, termed the position distribution of the team. Computing the position distribution for each frame provides a sequence of distributions, which we process to extract motion features for team activity recognition. The motion features are obtained at each frame using frame differencing and optical flow. We investigate the use of the proposed motion descriptors with Support Vector Machines (SVM) classification, and evaluate on a publicly available European handball dataset. Results show that our approach can classify six different team activities and performs better than a method that extracts features from the explicitly defined positions. Our method is new and different from other trajectory-based methods. These methods extract activity features using the explicitly defined trajectories, where the players have specific positions at any given time, and ignore the rest of the playground. In our work, on the other hand, given the specific positions of the team players at a frame, we construct a position distribution for the team on the whole playground and process the sequence of position distribution images to extract motion features for activity recognition. Results show that our approach is effective.
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