Real-time vehicle counting using custom YOLOv8n and DeepSORT for resource-limited edge devices (original) (raw)
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Journal of Xi’an Shiyou University, Natural Science Edition, 2022
For tracking and detecting vehicles, use computer vision techniques. It is essential to traffic accident detection and intelligent transportation systems. On the highway an essential component of traffic surveillance is the detection, identification, and counting of vehicles. It takes a lot of effort to create a traffic monitoring model that performs well. Artificial intelligence-based traditional vehicle detection systems have weak detecting capability and robustness. A deep learning model for vehicle detection, tracking, and counting is proposed in this paper and is based on an efficient Yolov7 single shot detector and Deep-Sort of Multi Object Tracking algorithms. The suggested model examines the automobile detection algorithms and suggests proposed detection models using moving vehicle footage as survey data. When observed under a range of circumstances, such as high traffic, nighttime, many vehicles overlapping, and part of the vehicle missing, the suggested identification system exhibits excellent adaptability. The algorithm can accurately detect and identify automobiles based on their edge outlines, according to experimental data. YOLOv7-DeepSORT performs higher in tracking accuracy after experimental evaluation as compared to the earlier YOLOv5-DeepSORT.
2020
Deep Learning based networks especially Convolutional Neural Network (CNN) models are widely used in vehicle detection, classification and counting system. On the other hand, transfer learning is a process of re-using a trained model to solve a problem similar to the one it was trained. Two ways of implementing transfer learning are direct usage of a model as a classifier and usage of a pre-trained model as a weight initialization for training with a new dataset. With recent development in the field of deep learning, many CNN models and architectures are available which makes the selection of a suitable model for performing vehicle detection, classification and counting a big challenge. Besides that, a tracking method is also required to track the vehicles in the video sequences so that the counting can be done as accurate as possible. In this project three types of CNN models i.e. SSD Inception, Faster R-CNN ResNet and Yolo DarkNet were tested on 10 traffic video samples using tran...
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Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.
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This paper aims to develop a method that can accurately count vehicles from images of parking areas captured by smart cameras. To this end, we have proposed a deep learning-based approach for car detection that permits the input images to be of arbitrary perspectives, illumination, and occlusions. No other information about the scenes is needed, such as the position of the parking lots or the perspective maps. This solution is tested using Counting CNRPark-EXT, a new dataset created for this specific task and that is another contribution to our research. Our experiments show that our solution outperforms the stateof-the-art approaches.