![]() Therefore, in this paper, we propose an unsupervised and concise method based on the features learned from a deep convolutional neural network to refine the traditional background subtraction methods. In contrast, the traditional background subtraction methods have low computational costs and can be applied to general scenes. However, most of these deep methods are supervised, only available for a certain scene, and have high computational cost. Recently, background subtraction methods have been developed with deep convolutional features, which have improved their performance. The computational complexity reduction of the proposed framework increases with the complexity of the considered CNN model’s architecture (e.g., 30.6% for YOLOv5s with 7.3M parameters 52.2% for YOLOv5x with 87.7M parameters), without undermining accuracy.Īdvancing the background-subtraction method in dynamic scenes is an ongoing timely goal for many researchers. The experiments were conducted using a large-scale dataset. Four state-of-the-art CNN-based detection architectures were benchmarked as base models of the detection cores to evaluate the proposed framework. A CNN-based detector with the appropriate number of convolutions is then applied to each image-candidate to handle the overlapping problem and improve detection performance. The BS-based module generates image-candidates containing only moving objects. ![]() In this paper, we propose a framework to reduce the complexity of CNN-based AVS methods, where a BS-based module is introduced as a preprocessing step to optimize the number of convolution operations executed by the CNN module. On the other hand, Background Subtraction (BS)-based approaches are lightweight but provide insufficient information for tasks such as monitoring driving behavior and detecting traffic rules violations. Although these methods provide high accuracy, they are computationally expensive. There has been significant interest in using Convolutional Neural Networks (CNN) based methods for Automated Vehicular Surveillance (AVS) systems.
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