Real-time Human Body Detection in Surveillance Footage Using Computer Vision Algorithms

Abstract

Abstract The detection of human bodies in real-time surveillance footage is a crucial problem in the field of computer vision with various applications in security, safety, and monitoring. In this paper, we present our computer vision system designed for real-time human body detection in surveillance footage. The system is optimized for both efficiency and accuracy, making it suitable for use in real-world scenarios. We have divided the system into several stages including data collection, feature extraction, model selection, model training, real-time deployment, and post-processing. To evaluate our system, we conducted thorough evaluations on a large dataset of surveillance footage and found it to achieve high accuracy and efficiency. The results of our extensive evaluation demonstrate the tool's effectiveness and potential for widespread use in various applications. The proposed system is a valuable tool for practical applications, providing a reliable and cost-effective solution for real-time human body detection and can be easily integrated into existing surveillance systems. Our contribution to the advancement of computer vision and its applications in real-world scenarios, particularly in the field of security and surveillance, is significant. The paper concludes with a discussion of future work aimed at further improving the tool's capabilities and applications.