Abstract
In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.
Highlights
Computer vision is a widely exploiting research area in the applications like automation system, robotics, Optical Character Recognition, human-machine interface, Natural Language Processing, and video analysis [1]
This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle
Background subtraction works by initializing a background model, difference between current frame and presumed background model is obtained by comparing each pixel of the current frame with assumed background model colour map
Summary
Computer vision is a widely exploiting research area in the applications like automation system, robotics, Optical Character Recognition, human-machine interface, Natural Language Processing, and video analysis [1]. Moving item tracking is an important research area in the computer vision applications. The finding and tracking of the moving target is essential in several applications, in video surveillance system. This research has made an experiment on tracking and finding moving vehicles at the video surveillance scenario. The objects that are present in the video system can be determined by moving object techniques. This design supports the automatic finding and tracking of the moving vehicles from the video. Tracking of the moving vehicles is used to detect the objects frame by frame in video. Background and foreground Subtraction technique is deliberated to be one of the most reliable methods for moving vehicle finding. Dynamic autoregressive moving average is specially considered for background modelling for the proposed work
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