Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd

We propose a novel method of abnormal crowd behavior detection in surveillance videos. Mainly, our work focuses on detecting crowd divergence behavior that can lead to serious disasters like a stampede. We introduce a notion of physically capturing motion in the form of images and classify crowd behavior using a convolution neural network (CNN) trained on motion-shape images (MSIs). First, the optical flow (OPF) is computed, and finite-time Lyapunov exponent (FTLE) field is obtained by integrating OPF. Lagrangian coherent structure (LCS) in the FTLE field represents crowd-dominant motion. A ridge extraction scheme is proposed for the conversion of LCS-to-grayscale MSIs. Lastly, a supervised training approach is utilized with CNN to predict normal or divergence behavior for any unknown image. We test our method on six real-world low- as well as high-density crowd datasets and compare performance with state-of-the-art methods. Experimental results show that our method is not only robust for any type of scene but also outperform existing state-of-the-art methods in terms of accuracy. We also propose a divergence localization method that not only identifies divergence starting (source) points but also comes with a new feature of generating a ‘localization mask’ around the diverging crowd showing the size of divergence. Finally, we also introduce two new datasets containing videos of crowd normal and divergence behaviors at the high density.