Intelligent Identification of Security Intrusions Around Wind Farms Based on Video Surveillance and Target Tracking

Enhanced deep learning model for anomaly object detection and tracking from surveillance videos.

Video anomaly detection plays a crucial role in video surveillance, which identifies suspicious intruders without human intervention. Moreover, the rapid growth of video surveillance applications such as intrusion detection, health monitoring systems, and fault detection provides a secure environment. Furthermore, detecting anomalous intruders from video is a challenging task because of diverse contexts, lack of training data, and environmental variations. Several conventional techniques use various Deep Learning algorithms for anomaly detection, which possess limitations including high false positive rates and occlusion. Therefore, to overcome the drawbacks, efficient anomaly object detection and tracking system is proposed using an enhanced wolf Crocuta optimization-based deep Bidirectional Long Short-Term Memory (EnWC-DBiLSTM) classifier. Here, an effective keyframe selection is attained by the Timber Prairie Wolf Optimization (TPWO) strategy, which optimally selects the required keyframes for further processing. Further, the combination DBiLSTM classifier processes the input data accurately and detects the target object, in both directions. Moreover, the enhanced Wolf Crocuta optimization (EnWC) helps to eliminate local power resolution, which improves the convergence speed of the model. Henceforth, the proposed model achieved an accuracy of 98.226%, equal error rate, sensitivity, and specificity of 1.774, 98.111%, and 99.551%, respectively, for the ShanghaiTech campus dataset.

IntelligentVehicle Security: Real-Time Anomaly Detection and Anti-Theft Surveillance Using Monocular Depth Estimation and Behavioral Analysis

A proactive, real-time computer vision system designed to detect potentially suspicious behavior around parked vehicles, with a specific focus on unauthorized proximity and loitering is proposed, making it a strong candidate for practical urban vehicle monitoring, subject to further large-scale validation across diverse environments.

Detection of Network Irregularities Using Machine Learning Techniques

REAL-TIME WEAPON AND THREAT DETECTION USING YOLOV12 WITH MULTI-SENSOR FUSION FOR ENHANCED SURVEILLANCE SYSTEMS

This study proposes an Advanced Surveillance Framework that makes use of YOLOv10, a next-generation real-time object detection algorithm that greatly outperforms conventional single-sensor approaches in precision, recall, and real-time responsiveness.

Enhancing anomaly detection in video surveillance with spatio-temporal enhanced deep associative memory networks

In the period of extensive video data generation from various surveillance sources, ensuring public safety and security is vital. Detecting unusual crowd behavior is essential, especially in scenarios with large gatherings. However, despite widespread video surveillance, incidents like vehicular accidents, stampedes, and burglaries still occur due to the limitations of traditional surveillance systems. In anomaly detection, finding the critical deviated pattern is the main and critical task. In the context of intelligent video surveillance, automated detection of abnormal behavior is achieved through computer vision analysis, eliminating the need for constant human monitoring. This paper proposes the spatio-temporal enhanced deep associative memory networks (STEAD)-network, a novel approach for anomaly detection in video sequences. The STEAD-network combines various techniques, including spatio-temporal enhancement, associative memory modules, and pattern recognition, to effectively capture and recognize abnormal events. Three benchmark datasets, UCSD Ped2, CUHK Avenue, and ShanghaiTech are used to evaluate this proposed dataset and to compare with existing state-of-the-art techniques. The results demonstrate that the STEAD-network consistently outperforms other methods, achieving significant improvements in anomaly detection accuracy across all datasets. The development of intelligent video surveillance systems is aided by this research by enhancing their ability to autonomously and accurately detect abnormal behavior in real-world scenarios.

Intelligent EfficientNetB7-based Surveillance Video Abnormality Classification Approach with Object Detection and Tracking using YOLOv9 with DeepSORT

Video surveillance systems help in tracking and monitoring the real-time events and also anomalous activities. An automated object detection and tracking poses security concerns that minimize the reliance on human intervention. Recent deep learning models provides high productivity and accuracy in dealing with videos of different qualities, however, as surveillance videos have less resolution and poor visibility, more rigorous strategies are required for better tracking of anomaly events. This research introduces object detection and tracking model based on abnormal recognition, enabling better security in crowded areas. Initially, the required videos are gathered through public online databases. The collected videos are directly fed into the object detection and tracking module, where YOLOv9 with DeepSORT (Yv9-DSORT) model is designed to track the objects across the video frames. The detected and tracked frames of the object are passed to the abnormal classification stage, whereas the EfficientNetB7 model is employed to provide abnormality classification results. The classification model identifies complex spatio-temporal patterns and small variations in abnormal regions for threat detection. The developed approach precisely identifies the unusual events. The resultant classified outcomes are validated with the baseline models to ensure its effectiveness.