Computer Vision Data Science

Object Tracking | Computer Vision | Deep Learning

Object Tracking

Object Tracking with Computer Vision

Research institutions and colleges in the United States and elsewhere have turned to video-based object tracking algorithms due to the rapid growth of computer vision technology. Normally, object tracking technology uses a robust model built from information about the object and its background in a video to predict the object’s size and shape, position, trajectory, and other motion states in the video. This allows for more advanced tasks such as the ability to predict a subject’s behavior or to understand a scene.

In addition to video surveillance and autonomous driving, military guidance, UAV reconnaissance, intelligent transportation, and human-computer interaction are some of the domains in which object tracking is being used. Important research value is associated with it.

Many successful object tracking methods have been presented in the last few years. Tracking algorithms are often split into generative algorithms and discriminative algorithms according to the distinct judgment procedures used in algorithm development. Discriminative tracking algorithms are the focus of current research. They have steadily gained dominance in the field of visual object tracking and produced several good study models.

Problem for Object Tracking

We focus on tracking a single item in a complicated movie. Features model, motion model, observation model, and online updating mechanism make up the basic framework of the single object tracking algorithm. Each component has a specific function. Each of the four characteristics reinforces the others. Using image processing technology, the feature model is meant to collect information about the object’s look and serve as a basis for the observation model.

The features suitable for object tracking include grey features, color features, histograms of oriented gradient features, deep features, and so on; the motion model primarily provides a set of candidate states that the object may appear in the current frame based on the object’s context information; the observation model’s role is to predict the state of the object based on the candidate state predictions.

For installation read the Readme file from the download folder

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