Abstract
The detection and tracking of objects in computer vision systems is a critical and challenging subject. Object identification and tracking currently has applications in a wide range of industries, including surveillance, autonomous robot navigation and vehicle navigation because to the increased accessibility of computing power and vast public datasets. When analyzing an image, the object tracking algorithm goal is to divide the region of interest, monitor its motion and location and identify any blobs. Every tracking necessitates an object detection mechanism either in each frame or whenever an object appears for the first time in the video sequence. The availability of high performance processors and low cost-high quality video cameras has enhanced the need of automated object tracking video sequences. Multiple object Identification and tracking in a dynamic environment has recently become focus of computer vision research. In this paper we discuss about the various object detection and tracking algorithms.
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