Abnormal Event Detection using Convolutional LSTM
K. Ramesh Babu, Jeraemius Shannon J, Trishi Tanmay · 2024
The development of smart CCTV systems heavily relies on the integration of abnormal event detection, human behavior analysis, and object recognition capabilities. These systems are designed to efficiently identify and respond to abnormal occurrences, suspicious behaviors, and potential threats within their monitored environments. Leveraging advanced technologies such as Machine Vision and Machine Learning, smart CCTV systems can effectively detect and classify anomalies present in the video feed. Typically employing frame-by-frame processing, these systems traditionally rely on Supervised Learning for training purposes; however, due to the diverse nature of anomalies and the impracticality of pre-training for all potential scenarios, there’s a gradual shift towards utilizing Unsupervised Learning and Semi-Supervised Learning methods. This transition aims to reduce the manual workload required for anomaly detection, thus enabling operators to focus on critical tasks. Moreover, to optimize storage efficiency, smart CCTV systems store abnormal events in high-quality formats while archiving normal scenarios in lower-quality formats. Additionally, these systems can extend their functionality through the implementation of distributed abnormality classification systems, where only abnormal events are transmitted to dedicated classifiers, streamlining the process and enhancing overall system performance. In essence, smart CCTV systems represent a sophisticated approach to surveillance, combining advanced technologies and intelligent algorithms to enhance security measures and minimize human intervention in anomaly detection and response processes.