Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 2083-2089· 0 citations· 21 references
Abstract
Surveillance systems have experienced rapid growth which results in production of large video data streams. The monitoring process for this data becomes challenging because its volume exceeds human capacity and this situation creates potential for errors. Our research presents a hybrid intelligent surveillance system which conducts automatic video analysis through its two core operational components. The system employs two primary components to achieve its objectives. The SlowFast-based model enables users to track activities through their development across various time intervals. The system employs YOLO-based models to identify critical objects which include fire and weapons and road accidents through real-time monitoring. The system achieves improved stability through the implementation of a temporal debouncing method. The system uses multiple frame detection checks to improve detection accuracy which helps prevent false alarms. The system includes a module dedicated to video summarization which creates a summary from detected activities and visual changes. The system discards unneeded video content while retaining essential information through this process. The model uses a dataset that contains 4758 video clips which display various classification types. The system reaches 85% validation accuracy which demonstrates its ability to handle new data successfully. The system operates on devices with limited resources while providing an immediate alert system to inform users about essential incidents. The system delivers an easy-to-use and effective solution for intelligent video surveillance operations.
The article compares the performance of traditional machine learning techniques with recent deep learning architectures such as CNNs, RNNs, TCNs, and Transformers, based on accuracy, computational cost, and suitability for real-world disorderly plotting.
Disha Deotale, Madhushi Verma, P. Suresh et al.· Discover Artificial Intellig...· 0 citations
A generative AI-based traffic surveillance system leveraging large language models (LLMs) to enable timely and context-rich interpretation of traffic events, demonstrating the system's effectiveness in producing context-aware traffic scene descriptions, improving operational decision-making, and enhancing roadway safety.
Video-based anomaly detection seeks to discover anomalous events, such as crimes, fires, or medical emergencies, by utilizing both spatial and temporal features of video data. Traditional surveillance systems are frequently limited to minimal recording, requiring human analysts for post-event assessment, resulting in delayed responses during crucial occurrences. To address these issues, we present a multi-layered approach to detecting video anomalies that can deal with both temporal and spatial components of video data. The input video is initially obtained from the dataset and undergoes frame conversion. The extracted key frames are then preprocessed for further analysis. To obtain multi-scale spatial characteristics from each frame, the first layer uses a spatial Pyramid pooling network (SPP-Net) along with a convolutional neural network (CNN). These spatial features are then passed to an optimized bi-directional gated recurrent unit (Opt-Bi-GRU) enhanced with Multi-Head Self-Attention (MHSA), which analyzes the temporal dynamics and captures both forward and backward dependencies across frames. Finally, a capsule network (CapsNet) processes the output of the Bi-GRU, identifying complex patterns that may indicate abnormalities over time. The proposed method is implemented using Python. The proposed model performs better than existing methods in terms of F1-score, specificity, sensitivity, accuracy, recall, precision, FPR, and FNR. The proposed model achieves the highest accuracy of 98.2%, 98.87%, and 98.52%, respectively, utilizing the UBI-fights, UCF-crime, and UCSD pedestrian datasets. These results demonstrate that the proposed framework provides an automated, reliable, and effective solution for real-time anomaly detection in surveillance systems.
M. Rao, Priyesh Kumar· International Journal of Com...· 0 citations
An enhanced wolf Crocuta optimization-based deep Bidirectional Long Short-Term Memory (EnWC-DBiLSTM) classifier is proposed using an enhanced wolf Crocuta optimization-based deep Bidirectional Long Short-Term Memory (EnWC-DBiLSTM) classifier for anomaly object detection and tracking.
B. Gayal, S. Patil, D. Meshram et al.· Scientific Reports· 0 citations
The proposed STEAD-network combines various techniques, including spatio-temporal enhancement, associative memory modules, and pattern recognition, to effectively capture and recognize abnormal events, and consistently outperforms other methods in anomaly detection accuracy across all datasets.
Traffic surveillance systems play a crucial role in intelligent transportation by enabling automated monitoring, traffic prediction, and accident detection. However, recognizing complex traffic scenarios from real-world videos remains challenging due to dynamic environments and temporal dependencies. This paper proposes a unified spatio-temporal framework that integrates YOLOv8 based object detection, convolutional neural networks for spatial feature ex traction, and long short-term memory networks for temporal modeling. Traffic videos are preprocessed to enhance visual consistency, and detected objects are transformed into structured spatial representations. Temporal dependencies across video sequences are learned using LSTM networks, and the extracted features are evaluated using multiple machine learning classifiers under different preprocessing strategies. Experimental results demonstrate that Z-score standardization improves classification performance, with Support Vector Ma chine achieving 63.27% accuracy and an F1-score of 55.66% in an eight-class traffic scenario classification task, indicating the feasibility and robustness of the proposed framework in real-world traffic environments.
Dhartee Patel, Jinal Ahir, Namrata Shroff et al.· ITEGAM- Journal of Engineeri...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
