Object Tracking

Object Tracking

Object tracking is a crucial component of computer vision that enables machines to track and follow objects in motion. This algorithm allows you to estimate or predict the position and other relevant information of moving objects in a video or image sequence. Object tracking is a vital aspect of various applications such as security and surveillance, autonomous vehicles, video analysis, and many more.

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How does object tracking work in computer vision?

Typically, the initial stage in object tracking is object detection. The process of locating an object in a single photo or video frame is known as object detection. Several object identification techniques exist, including YOLO (You Only Look Once), Haar Cascade, and Faster R-CNN (Region-based Convolutional Neural Networks).

Object tracking uses the object's information from the object detection algorithm to follow an object in consecutive frames of a video or image sequence. In order to determine where an object will be in the current frame, object tracking algorithms use the position and motion data from prior frames.

Traditional or classical machine learning algorithms such as k-nearest neighbor or support vector machine were used in some of the methodologies – these approaches are effective at predicting the target object, but they require the extraction of important and discriminatory information by professionals. Deep learning algorithms, on the other hand, extract these important features and representations on their own. Some of the popular deep learning based object tracking algorithms are DeepSORT, MDNet, SiamMask, and many more. 

To know more about object tracking algorithms, please read the blog Object Tracking for Machine Learning and Computer Vision.