Deep Neural Network based Anomaly Detection for Real Time Video Surveillance

One of the main concerns across all kinds of domains has always been security. With the crime rates increasing every year the need to control has become crucial. Among the various methods present to monitor crime or any anomalous behavior is through video surveillance. Nowadays security cameras capture incidents in almost all public and private place if desired. Even though we have abundance of data in the form of videos they need to be analyzed manually. This results in long hours of manual labour and even small human discrepancies may have huge consequences negatively. For this purpose, a Convolution Neural Network (CNN) based model is built to detect any form of abnormal activities or anomalies in the video footages. This model converts the input video into frames and detects the anomalous frames. To increase the efficiency of the model, the data is de-noised with Gaussian blur feature. The avenue dataset is used in this work to detect and predict various kinds of anomalies. The performance of the model is measured using classification accuracy and the results are reported

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupervised methods, they often overlook the emergence of weakly-supervised and fully-unsupervised approaches. To address this gap, this survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED). By skillfully incorporating recent advancements rooted in diverse assumptions and learning frameworks, this survey introduces an intuitive taxonomy that seamlessly navigates through unsupervised, weakly-supervised, supervised and fully-unsupervised VAD methodologies, elucidating the distinctions and interconnections within these research trajectories. In addition, this survey facilitates prospective researchers by assembling a compilation of research resources, including public datasets, available codebases, programming tools, and pertinent literature. Furthermore, this survey quantitatively assesses model performance, delves into research challenges and directions, and outlines potential avenues for future exploration.Comment: Accepted by ACM Computing Surveys. For more information, please see our project page: https://github.com/fudanyliu/GVAE