Analysis of Detection and Tracking Algorithms of Dot Objects in Video Stream
DOI:
https://doi.org/10.31649/1997-9266-2022-165-6-48-56Keywords:
target detection, target tracking, background, target, noiseAbstract
Computer vision is a relevant technology, that allows the machine, relying on external data from sensors, to independently make decisions on how to react to external factors. One of the possible problems that computer vision could solve is the automatic detection and tracking of point objects. For this purpose, in practice, a video surveillance system is often used, which allows to obtain a video stream of the viewed site. Digital image processing methods allow reliable and precision detection and tracking of various objects. Since the point object is maneuverable, the detection algorithm must be fast and robust to avoid false detections, which makes such algorithms complicated. The work presents an overview of known methods of detecting and tracking objects. The advantages and disadvantages of the methods based on the principle of "detect before track" and "track before detect" are presented. Methods that are based on "detect before track" principle detect the object in each frame, therefore do not require additional accumulation of information about the parameters of the searched object and after that the obtained results are transferred to the tracking algorithm. Methods based on "track before detect" principle first accumulate some frames to determine the trajectory of the object, after that a decision is made whether object detected or not. Such algorithms require a certain amount of data to process, which leads to a delay in obtaining results. The methods of tracking objects are considered. Such methods are based on the processing of coordinate information about the movement of objects. One of the crucial tasks for tracking is the filtering of object motion parameters. To estimate the condition and control a dynamic system with a random structure, it is advisable to apply the apparatus of mixed Markov processes in the discrete time. The following algorithms of trajectory filtering are considered: an autonomous multi-model algorithm, a generalized pseudo-Bayesian algorithm of the first order, a generalized pseudo-Bayesian algorithm of the second order, a multi-model algorithm with inter-model interaction.
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