Real-Time Object Detection in Video for Traffic Monitoring

Real-Time Object Detection in Video for Traffic Monitoring

Sai Deepak Alapati, Muthukumar Arunachalam, Chandana Chennamsetty, Pujitha Dantam, Anusha Dabbara
Copyright: © 2023 |Pages: 14
DOI: 10.4018/978-1-6684-7110-4.ch008
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This chapter presents the application of YOLO, a deep learning-based object detection algorithm, for traffic monitoring. The algorithm was applied to real-time video streams from roadway cameras to detect and track vehicles. The results were compared with traditional computer vision methods and showed superior accuracy and processing speed. This study highlights the potential of YOLO for traffic monitoring and the significance of incorporating deep learning into intelligent transportation systems. YOLO V7 outperforms all other real-time object detectors on the GPU V100 in terms of speed and accuracy in the range of 5 to 160 frames per second and has the highest accuracy of 56.8% AP. YOLO V7 also introduces a new training methodology that improves the convergence rate and the generalization capabilities of the model. Experimental results show that YOLO V7 outperforms existing methods in terms of accuracy, speed, and efficiency, making it an attractive solution for real-world applications.
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Traffic monitoring is a crucial aspect of road safety and transportation efficiency. The increasing number of vehicles on the roads has created a demand for efficient and reliable methods for monitoring traffic flow and detecting abnormal events. The traditional methods for traffic monitoring, such as background subtraction and object tracking, often fall short in terms of accuracy and processing speed. This has led to the search for more advanced techniques that can provide a better solution to the challenges faced in traffic monitoring.

In recent years, the field of computer vision has made significant progress with the advent of deep learning algorithms. These algorithms have been applied to various computer vision tasks, including object detection, classification, and segmentation. One such algorithm that has shown promising results in object detection is YOLO (You Only Look Once). YOLO is a fast and accurate object detection algorithm based on deep neural networks (Egodawela et al., 2020). It has been applied to a wide range of applications, including autonomous vehicles, security, and surveillance.

This study aims to evaluate the performance of YOLO in detecting and tracking vehicles in real-time video streams from cameras mounted on roadways for traffic monitoring purposes. The results of the study will be compared with traditional computer vision techniques to assess the advantages of using YOLO for traffic monitoring. The findings of this study will provide insights into the potential of using deep learning algorithms for traffic monitoring applications and highlight the significance of incorporating these algorithms into intelligent transportation systems.

Background: Real-time object detection in video for traffic monitoring is a computer vision application that involves detecting and tracking vehicles, pedestrians, and other objects in live video feeds captured by cameras installed on roads and highways. This technology is used to monitor traffic flow, detect incidents such as accidents or congestion, and provide real-time information to traffic management systems. Object detection algorithms such as YOLO, Faster R-CNN, and SSD are commonly used for this application, and deep learning models trained on large datasets are often employed to improve accuracy and robustness (Jiangzhou, 2021). Other techniques such as optical flow and background subtraction may also be used in combination with object detection to improve performance in challenging conditions.

Focus of the Article: The focus of an article related to real-time object detection in video for traffic monitoring. The focus of such an article would likely be on discussing the various object detection algorithms and techniques used in this application, their performance, and any challenges or limitations associated with them. The applications and benefits of real-time object detection in traffic monitoring, including improving road safety, reducing congestion, and enhancing traffic management systems. Additionally, the current trends and future directions in this field, including new algorithms, hardware and software advancements, and emerging applications.


Proposed System

In the proposed system the MS COCO instance segmentation dataset was used to refine the YOLO V7 object detection model, which was trained across 30 epochs. It achieves state-of-the-art real-time instance segmentation outcomes. An improved object detection performance, a more reliable loss function, and improved label assignment and model training efficiency are all provided by a quicker and more robust network design. The YOLO algorithm divides the image into N grids, each with an equal-sized S X S region.

Key Terms in this Chapter

Faster R-CNN: A real-time object detection system that uses a region proposal network to identify regions of interest in an image or video.

Single-Shot Detector (SSD): A type of object detection algorithm that uses a single neural network to detect objects in an image or video.

You Only Look Once (YOLO): A real-time object detection system that uses a single neural network to detect objects in an image or video.

Traffic Monitoring: The use of sensors and other technologies to monitor the flow of traffic on roads and highways.

Video: A sequence of images that are played back in rapid succession to create the illusion of motion.

Real-Time: Refers to systems that process data in near-real-time or with very low latency, such that the system can respond to new data quickly.

Convolutional Neural Network (CNN): A type of deep neural network that is commonly used for image and video processing tasks, including object detection.

Deep Learning: A type of machine learning that uses neural networks to model and solve complex problems.

Object Detection: A computer vision task that involves identifying and localizing objects in an image or video.

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