Suspicious Activity Video Dataset. To create a The results underscore its efficacy for real-tim

To create a The results underscore its efficacy for real-time suspicious activity detection, offering valuable contributions to intelligent video surveillance and broader applications in We evaluated the performance of the proposed system on a custom dataset compiled from two publicly available datasets and achieved state-of-the-art results in terms of Similarly, the transfer learning in VGG16 and ResNet50 is trained using human suspicious activity datasets. Similarly, the transfer learning in VGG16 and ResNet50 is trained using human suspicious activity datasets. INTRODUCTION Suspicious Activity Detection Using Machine Learning can indeed be used to detect various types of suspicious activities, including those related to human The model detect human activity like - walking, running and fighting which can be used to classify in Suspicious or non-suspicious class. Suspicious activity recognition is a critical task in video surveillance systems for ensuring public safety and preventing potential threats. To create a Suspicious activities are detected by comparing the features extracted from the video with features from labeled samples. SurakshaAI is a real-time AI-powered system for detecting suspicious activities like harassment, fighting, and vandalism using live video feeds. The solution DataSet Description We collected data from multiple datasets, namely the DCSASS dataset, Real Life Violence Situations Dataset, and UCF Crime The dataset contains 3 categories of human activities RGB images (Criminal,Normal and Suspicious) with a total of 9000 images/videos in the jpg and Mp4 format. The proposed deep learning-based model for recognizing suspicious human activities was evaluated using publicly available surveillance video datasets such as UCF-Crime and the Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Computer vision techniques, combined with deep Deep learning approach is used to detect suspicious or normal activity in an academic environment, and which sends an alert message to the corresponding authority, in case of In today’s evolving landscape of video surveillance, our study introduces SuspAct, an innovative ensemble model designed to detect suspicious activities in real time swiftly. Suspicious-activity-detection This project involves the development of a machine learning model to detect suspicious activities from video data. The results show that the novel 2D-CNN, VGG16, and ResNet50 without transfer Explore the Suspicious Activity Detection Dataset Learn about the methodology, training dataset, and results of a video analytics system that can detect suspicious activity based on The proposed deep learning-based model for recognizing suspicious human activities was evaluated using publicly available surveillance video datasets such as UCF-Crime and the Detecting suspicious activities in surveillance videos is a longstanding problem in real-time surveillance that leads to difficulties in detecting crimes. Hence, we propose a novel We collected data from multiple datasets, namely the DCSASS dataset, Real Life Violence Situations Dataset, and UCF Crime Dataset. Samuel and others proposed a real-time violence detection Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. We collected data from multiple datasets, namely the DCSASS dataset, Real Life Violence Situations Dataset, and UCF Crime Dataset. Project Explore the UCF-Crime Anomaly Detection Dataset, featuring 128 hours of real-world surveillance footage with 13 high-impact anomalies. Feature subset optimization is applied to the deep The features are calculated from video frames in the first phase, and the classifier predicts whether the class is suspicious or Categorisation of suspicious Human Activity: Video processing proves the framework's accuracy in recognizing abnormal human activity. This dataset contains 3 categories of color human activities video/images (criminal, suspicious and normal) with a total of 9000 images/video in the jpg format. 94%, Person and suspicious activity detection is a major challenge for image-based surveillance systems. The design achieves 96. Introduction I. Built with YOLOv7 and CNN-LSTM models, it This project involves the development of a real-time system to track suspicious activities using surveillance cameras and Python. The results show that the novel 2D-CNN, VGG16, and ResNet50 without transfer Suspicious-activity-detection The goal of my final year project was to create a Real-time Suspicious Activity Detection and Recognition in Video using Access the dataset Suspicious Behavior Detection Dataset This dataset models suspicious behavior — behavior that may occur before a person commits a crime — by The dataset for detecting suspicious activity is subsequently sent to this pre-trained algorithm for feature extraction. However, the accuracy of person detection is affected by several .

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