VisionDrive: Smart Traffic Analysis with YOLOv7 and DeepSORT
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VisionDrive: Smart Traffic Analysis with YOLOv7 and DeepSORT
Jagdish Pimple1+, Mohit Agarwal2, Dipmala Kathale3, Kajal Matte4, Rutuja Hande5, Shruti Jiwane6
1Assistant Professor, Department of Information Technology, St. Vincent Pallotti College of Engineering and Technology, Nagpur
2Associate Professor, School of Computer Science Engineering and Technology, Bennett University, Greater Noida
3,4,5,6 Student of Department of Information Technology, St. Vincent Pallotti College of Engineering and Technology, Nagpur
ABSTRACT
This research introduces a novel vehicle division mechanism intended for dynamic metropolitan settings. Modern technology, including deep learning, computer vision, and real-time warning systems, reduces traffic, enhances road safety, and effectively manages unanticipated situations. A strong vehicle instance segmentation model that can recognize cars in real-time from many sources is first developed using the YOLO technique. In the next round, the cars are tallied and a predetermined threshold is established. If there are more cars in the video or image than a preset threshold, the system will identify this and assume that traffic is heavy. During the last stage, users receive information on congestion analysis and anomaly identification through user-friendly interfaces. This article aims to modernize traffic control by merging cutting-edge technologies with all-encompassing techniques to deliver a responsive, safe, and efficient urban transportation experience.
KEYWORDS: YOLOv7, DeepSORT, OpenCV, Object Detection, Multiclass Segmentation, Object Counting.
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