The management of these cars gets more difficult as the number of vehicles on the road rises. Planning, observation, and management must be synchronised for safety and to ease traffic. It is essential for all parties involved to work together, including the government, transportation providers, and drivers. Transportation efficiency depends on effective management. A video-based vehicle is required for the collection and analysis of such video without interfering with traffic to deploy a system without disturbing the infrastructure and identify traffic accidents and congestion. In this study, we have developed a remedy for the aforementioned issue using video surveillance and taking into account the video data from the traffic cameras. We used a Gaussian-based background reduction method, an adaptive thresholding strategy, and tracking techniques like blob tracking and a virtual detector. Python's OpenCV tool was used to carry out the implementation. Our proposed system is capable of object recognition, congestion tracking, and precise item counting. In this era people utilizing autos is growing increasing day by day. The planning, observing, and controlling of these vehicles is proving to be a very difficult undertaking. A video-based vehicle is required for the collection and analysis of such video without interfering with traffic to deploy a system without disturbing the infrastructure and identify traffic accidents and congestion. In this study, we have developed a remedy for the aforementioned issue using video surveillance and taking into account the

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