Authors: Ms. Haripriya, Vishva P, Varunkumar V.
Abstract: The Moving Object Detection System is a project that is based on vision and is aimed at the real-time identification and tracking of moving objects by applying the processing of images. The system can either analyze live video streams or recorded footage in order to detect the motion by the method of comparing the differences between two consecutive frames. The system that is executed on Python together with the OpenCV library performs background subtraction, frame differencing, and contour detection to find and draw the moving objects accurately. The technology has great potential in fields like surveillance, traffic management, and automation of security systems. The system is capable of detecting motion with high efficiency, requiring very little computational resources, and at the same time it is very easy to integrate with IoT and alert systems for added functionality. The system in question represents an efficient solution that can be applied in various contexts not only for motion detection but also for its cost-effectiveness and versatility. Moreover, the proposed system can handle indoor and outdoor setup variations like light changes and background noises through the use of filtering and thresholding techniques. Thus, it can already be a low-budget, real-time, and powerful solution for motion tracking in smart surveillance and safety applications. Further improvements can introduce the application of deep learning for the classification of detected objects and the provision of cloud-based alert systems that would further extend its range of use across various domains.
