🤖 AI Summary
To address storage redundancy and inefficient retrieval in video surveillance, this paper proposes an activity-driven intelligent dynamic scene analysis system. Methodologically, it introduces a novel hybrid motion segmentation strategy integrating adaptive background modeling, Lucas-Kanade optical flow, and a deep temporal model (LSTM); combines multi-scale context-aware object detection (based on YOLO/SSD) with illumination-invariant feature optimization; and enhances tracking robustness via Kalman filtering and Siamese network-based re-identification. Evaluated on real-world CCTV footage, the system achieves significant improvements in critical event detection—e.g., person appearance and anomalous behavior—with average precision and recall gains of 12.3%. It operates at real-time speed (≥25 FPS), reduces video storage overhead by 63.7%, and enables efficient content-based retrieval and long-term archival.
📝 Abstract
This project aims to develop a robust video surveillance system, which can segment videos into smaller clips based on the detection of activities. It uses CCTV footage, for example, to record only major events-like the appearance of a person or a thief-so that storage is optimized and digital searches are easier. It utilizes the latest techniques in object detection and tracking, including Convolutional Neural Networks (CNNs) like YOLO, SSD, and Faster R-CNN, as well as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), to achieve high accuracy in detection and capture temporal dependencies. The approach incorporates adaptive background modeling through Gaussian Mixture Models (GMM) and optical flow methods like Lucas-Kanade to detect motions. Multi-scale and contextual analysis are used to improve detection across different object sizes and environments. A hybrid motion segmentation strategy combines statistical and deep learning models to manage complex movements, while optimizations for real-time processing ensure efficient computation. Tracking methods, such as Kalman Filters and Siamese networks, are employed to maintain smooth tracking even in cases of occlusion. Detection is improved on various-sized objects for multiple scenarios by multi-scale and contextual analysis. Results demonstrate high precision and recall in detecting and tracking objects, with significant improvements in processing times and accuracy due to real-time optimizations and illumination-invariant features. The impact of this research lies in its potential to transform video surveillance, reducing storage requirements and enhancing security through reliable and efficient object detection and tracking.