Real-Time Object Tracking in Videos

Real-time object tracking in videos is an engaging mini project that leverages AI and machine learning techniques to identify and follow objects across video frames. The project begins with data collection, using video datasets containing various moving objects. Utilizing frameworks like OpenCV and deep learning libraries such as TensorFlow or PyTorch, the project can implement algorithms like YOLO (You Only Look Once) or SSD (Single Shot Multibox Detector) for accurate object detection. The core of the project involves combining detection with tracking algorithms like Kalman filters or SORT (Simple Online and Realtime Tracking) to maintain the identity of the objects over time. The output can be visualized by overlaying bounding boxes around detected objects, updating in real-time as the video progresses. This project not only enhances understanding of computer vision concepts but also provides practical experience with real-time processing, making it a valuable addition to any AI enthusiast’s portfolio.