Conditional autoregressive-tunicate swarm algorithm based generative adversarial network for violent crowd behavior recognition

Violent crowd behavior detection has gained significant attention in the computer vision system. Diverse crowd behavior detection approaches are introduced to detect violent behavior but enhancing the recognition rate poses a complex task due to different crowd diversity, mutual occlusion between crowds, and diversity of monitoring scene. Therefore, a crowd behavior recognition mechanism is introduced by Conditional Autoregressive-Tunicate Swarm Algorithm based Generative Adversarial Network (CA-TSA based GAN) to detect violent behavior. Accordingly, the developed CA-TSA is modeled by inheriting Conditional Autoregressive Value at Risk by Regression Quantiles with Tunicate Swarm Algorithm. Initially, the features, such as Tanimoto based Violence Flows descriptor, Local Ternary patterns, and Gray level co-occurrence matrix are extracted from the video frames. Then, the crowd behavior recognition is done by the GAN, which finds the abnormal and the normal crowd behaviors. Here, GAN is trained by the proposed CA-TSA. Moreover, the performance of the proposed method is analyzed using ASLAN challenge dataset. The developed model has the accuracy, sensitivity, and specificity values of 93.688%, 94.261%, and 94.051%, respectively.