The dramatic ascent in advancements and associated applications have generally revived the scholarly community enterprises to accomplish more effective and strong answer to satisfy contemporary needs. Reconnaissance frameworks have drawn in mainstream researchers to empower occasions to settle on convenient choice interaction. This paper proposes a robust model to perform multi-class classification using Evolutionary Computing algorithm named Binary Bat Algorithm based Multi-Feed Forward Neural Network model (BBA-MFNN). Here, the utilisation of GLCM and AlexNet features provides deep spatiotemporal feature information helps to make optimal classification decision and the proposed BBA-MFNN algorithm enables optimal multi-class classification while avoiding local minima and convergence problem which can often be present in video data analysis due to non-linear feature distribution. Thus, the proposed model accomplishes Crowd Behaviour analysis with an accuracy of 96.15%, precision of 94.66%, 96.52% recall and F-Measure of 95.56%, which is higher than the classical MFNN classifier

Crowd Behaviour Analysis, Hybrid Deep Features, GLCM, AlexNet, Binary Bat Algorithm, Feed Forward Neural Network