Deep learning based violence detection

substantial attention, both in RGB and depth maps. However, the identification of aggressive movements within video sequences remains an area that is relatively underexplored and contemporaneous. Despite the omnipresence of camera systems across diverse spheres of human life, there exists a palpable dearth in research pertaining to the nuanced analysis of these visual data streams. The escalating ubiquity of cameras has precipitated an exponential accumulation of data, thereby engendering a pressing exigency for sophisticated systems capable of discerning intricate human activities. This project endeavors to craft an innovative violence recognition paradigm tailored for integration within video annotation systems, specifically designed for camera setups. The proliferation of technology has facilitated unfettered access to copious volumes of video data owing to the burgeoning internet bandwidth. The meticulous annotation of video segments portraying acts of violence assumes paramount significance, especially within the domains of security and content-driven video retrieval systems. Security cameras, however, evince limitations in accurately discerning violent acts, while the task of exhaustive monitoring by human operators in expansive camera networks approaches insurmountability. Consequently, the identification of violence within video footage has emerged as a critical concern This project aspires to forge a novel deep learning-driven violence detection mechanism that proffers superior efficacy when juxtaposed against prevailing methodologies. The present study methodically employs Transfer Learning in conjunction with a Long Short Term Memory (LSTM) network specifically tailored for video frames. Leveraging MobileNetV2 facilitates the extraction of spatial features from successive video frames. Concurrently, BILSTM endeavors to preserve localized spatial attributes while meticulously scrutinizing temporal interrelations inherent within video frames. In this study, the best result was obtained by using the hockey fight data set, with a value of 99.37%, eclipsing the performance benchmarks set forth by antecedent studies.