Object detection involves identifying the presence and location of one or more objects within an image or video. This is typically done using a pre-trained object detection model, which has been trained on a large dataset of labeled images to recognize various objects. The model outputs bounding boxes around each detected object, indicating its position within the image.

Object tracking, on the other hand, involves following the movement of one or more objects over time within a video stream. This can be done using a variety of algorithms, which typically involve analyzing the movement and appearance of objects in consecutive video frames and matching them to previously detected objects.

Object detection and tracking are important for a wide range of applications, including surveillance, self-driving cars, robotics, and augmented reality. They can also be used to improve the accuracy and efficiency of other computer vision tasks, such as image classification and semantic segmentation.