## 1 Introduction
### Contribution  
The main contribution of vehicle detection and tracking is to provide accurate and real-time data on the movement and behavior of vehicles on roadways. These data can be used to optimize traffic flow and improve safety. In response to the above problems, integrating the Deep SORT and YOLOv5 ensures better accuracy for vehicle detection, tracking, and counting. In order to track several objects, Deep SORT is the first choice of researchers as one of the best leading tracking algorithms due to its robustness and speed [19]. [PERSON] et al. [20] integrated the YOLO model with the Kalman filter to track the objects for counting. However, this system was not efficient in real-time detection and tracking due to the constraints in speed. The integration of YOLOv5 and Deep SORT for vehicle detection and tracking offers several novel aspects and advantages compared to traditional approaches. Here, there are some key points highlighting the novelty of this proposed work:  
* We propose an effective vehicle detection and tracking scheme with the fusion of deep sort and Yolov5 in a real-time traffic management system. * The detection of vehicles using YOLOv5 to identify the current location of vehicle using a bounding box and classification. * Deep SORT algorithm tracks the vehicles after identifying the bounding box by the YOLOv5 model for tracking the vehicles and counting their vehicles. The same algorithm will also strengthen the vehicle detection and tracking to reduce erroneous detections and missed detections relying on extraneous factors. * Assigning unique IDs to each vehicle to monitor the traffic flow for vehicle tracking and counting while passing through hot zones and virtual lines. Vehicle detection using YOLOv5: YOLOv5 is an advanced object detection algorithm that is widely recognized for its exceptional precision and real-time capabilities. It leverages a deep neural network to directly forecast the bounding boxes and class labels of objects present within an image. This approach eliminates the need for additional post-processing steps, resulting in efficient and accurate vehicle detection. By integrating YOLOv5 into the proposed work, it enables efficient and precise vehicle detection, allowing for the real-time tracking of vehicles in a video stream or series of images. Deep SORT for Vehicle Tracking: Deep SORT (Deep Simple Online and Realtime Tracking) is a tracking algorithm that extends the capabilities of object detection algorithms by associating detected objects across frames and maintaining unique identities for each object. It leverages deep learning techniques to learn the appearance features of objects and uses them to track and match objects over time. Deep SORT provides robust tracking even in challenging scenarios with occlusions, object appearance changes, and crowded scenes. End-to-end vehicle detection and tracking: The proposed work integrates YOLOv5 and Deep SORT to create an end-to-end pipeline for vehicle detection and tracking. This integration allows for a seamless transition from object detection to object tracking, providing a comprehensive solution for monitoring and analyzing vehicle movements in real-world scenarios. The combined approach enhances the overall performance and accuracy of the system by leveraging the strengths of both algorithms. Real-time performance: YOLOv5 is known for its real-time inference speed, making it suitable for applications that require fast processing, such as video surveillance and autonomous driving. By integrating YOLOv5 with Deep SORT, the proposed work enables real-time vehicle detection and tracking, facilitating timely decision making and response in various domains, including traffic management, security systems, and intelligent transportation systems. Robustness and accuracy: Deep SORT enhances the tracking capabilities by considering the appearance features and motion information of vehicles over time.