Intelligent Surveillance System for Real-Time Detection of Anomalous Activities in Video Streams

Abstract

The increasing complexity of surveillance structures necessitates advanced techniques for monitoring big volumes of video statistics. This record opinions the utility of convolutional neural networks (CNN) and deep getting to know techniques for detecting suspicious pastime in video streams. The technique involves preliminary video statistics processing to extract key features, observed by training a CNN version to distinguish between normal and abnormal behaviours through recognizing spatial and temporal patterns within the video frames. techniques consisting of transfer gaining knowledge of and statistics augmentation are employed to enhance the model's generality and robustness. The effectiveness of this approach was validated via various checks, including experiments on datasets like u.s.a. Pedestrian and road, where the CNN-primarily based technique established high accuracy and go back quotes in figuring out suspicious activities. The scalability and actual-time processing abilities of the version make it adaptable to various tracking environments. These findings are great for the advancement of the surveillance era, offering a dependable technique for real-time detection of suspicious sports in video streams. The proposed CNN-based approach is promising for bolstering safety in public spaces, transportation structures, and important infrastructure, thus contributing to better safety features.