Taylor series based compressive approach and Firefly support vector neural network for tracking and anomaly detection in crowded videos
The application areas of multimedia content and computer vision analysis gains remarkable attention towards the motive to recognize the actions of the humans present in the video. Accordingly, crowd behavior analysis is important topic due to the significance of video surveillance in the public area. This work introduces an anomaly detection model by introducing a tracking model and the optimization based classifier for the crowd video. The objects present in the video require tracking since the anomaly depends on the action of the object. This work proposes a hybrid tracking model using the Taylor series based predictive tracking and the compressive tracking approach. The features are extracted from the tracked objects, and a feature vector is formed. Moreover, this work proposes the Firefly based support vector neural network (FSVNN) for the classification purpose. The weights of the proposed FSVNN classifier are trained with the genetic and the firefly algorithm. From the simulation results, it is evident that the proposed anomaly detection model with the FSVNN classifier attained overall better performance with the values of 0.97035, 1, and 0.96, for sensitivity, specificity, and accuracy, respectively.