Single Target Visual Object Tracking

In the present world, the evolution of technology takes place in terms of developing new things to solve some real-world problems. Almost all fields are creative in the sense of creating new things. Computer vision is one of the fields that give the technology that can help with different real-time problems. This field offers some useful applications over the last few decades. Different technologies like Autonomous cars, Traffic flow analysis, X-ray analysis, etc. make this field interesting for researchers. Object detection is one of the applications of Computer vision. Object detection and tracking enable the machine to detect the object and track the object through the entire scene. In this paper, a supervised learning approach for object detection is presented which consists of a detector, a Neural network, Images, and Groundtruths. The presented model successfully tracks the object and the dataset used for the performance evaluation is Online Object Tracking Benchmark (OOTB), Unmanned Aerial Vehicle (UAV), and Visual Object Tracking Benchmark (VOT) dataset.