A Real Time Multiple Object Tracking in Videos using CNN Algorithm

Object tracking serves as a fundamental task in the field of computer vision and machine learning. This assignment involves accurately determining the positions of objects across consecutive frames in a video sequence. The objective of this undertaking is to develop a system for detecting and tracking objects using advanced computer vision techniques. The end result of this project is a functional tracking system that combines optical and modern infra-red technologies. This system finds applications in domains like unsupervised surveillance and is notably reliable, capable of functioning independently or seamlessly integrating into more extensive frameworks. The primary aim of this research work is to create an object tracking system using Python and OpenCV, with the intention of providing an efficient and easily accessible solution. The system allows for the tracking of objects from various sources, including uploaded videos and live webcam feeds. In real-world scenarios, such as monitoring traffic on highways, it is often necessary to track multiple objects simultaneously. By employing object tracking algorithms, this project facilitates the simultaneous tracking of numerous objects, such as cars on a highway, enabling the extraction of valuable traffic-related information