A Traffic Surveillance System in Real-Time to Detect and Classify Vehicles by Using Convolutional Neural Network

Okaishi, Wahban Al; Zaarane, Abdelmoghit; Slimani, Ibtissam; Atouf, Issam; Benrabh, Mohamed

doi:10.1109/syscobiots48768.2019.9028037

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…W. AL OKAISHI proposed a real time traffic analysis for detection background subtraction method and classification using CNN. The work is carried out using stationary cameras for testing at intersections [2,16,17,18]. Fedorov, A addressed the problem of traffic flow estimation using video surveillance.…”

Section: Related Workmentioning

confidence: 99%

New Directions in Traffic Control Analysis through Video Surveillance

et al. 2021

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Traffic management is an increasing problem in both cities and sub urban areas. Authority people involved in traffic management system spend much of time in controlling traffic at junctions. With the advances in technology, monitoring traffic through image processing and video surveillance techniques became the researchers’ attention. These techniques help us in controlling traffic as well as to identification of kamikaze drivers and speed violators. The key focus of this research is to do traffic analysis using video surveillance to detect speedy drivers. A wide range of traffic parameters such as flow of traffic, speed of vehicles and vehicle registration number are the major components involved in this research. In this paper, traffic analysis is carried out based on streaming video data with YOLO tool. In this paper an eco system is developed for object detection, vehicle number detection and the speed of the vehicle using computer vision algorithms. With the application tool developed, traffic control authority people can warn the speedy drivers on the fly.

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Section: Related Workmentioning

confidence: 99%

New Directions in Traffic Control Analysis through Video Surveillance

et al. 2021

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“…Convolutional neural networks (CNNs) are one of the most remarkable successes in the deep learning area, which demonstrate superior performances on a large amount of computer vision tasks, such as detection [35][36][37], denoising [38], recognition [39,40], and better feature representation [41][42][43]. To our best knowledge, AlexNet, as the champions of the ImageNet competition in 2012, is one of the successful CNN designs developed on GPU.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning for Facial Beauty Prediction

et al. 2020

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Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts.

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“…The safety of people and property is at real risk as road accidents increase year on year and as a result traffic monitoring systems (Slimani et al, 2020;Slimani et al, 2019) and driver assistance systems (Zaarane et al, 2020;Zaarane et al, 2019b) have emerged and developed. Nowadays, vehicle detection (Al Okaishi et al, 2019;Zaarane et al, 2019a) is a very important subject because of its major role in reducing accidents and controlling traffic, especially on freeways. Different types of sensors are employed for detecting vehicles, e.g.…”

Section: Introductionmentioning

confidence: 99%

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

Slimani

Zaarane

Atouf

2023

Transport and Telecommunication Journal

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In this work, a day and night time vehicle detection system for traffic surveillance is proposed. Our system is composed of two main processes, day time and night time processes. In the night time, the vehicles are detected based on their taillights and headlights. First of all, the 2D-DWT (Two Dimensional Discrete Wavelet Transform) and the background subtraction are applied to the input image. Then, the connected component technique is used to extract the region of interest. If it is the daytime, the connected component candidates are taken as potential vehicles after applying a pre-processing algorithm to improve the result. If it is the night-time, a filtering operation is used to keep only the bright white and red connected component candidates (which represent potential headlights and taillights, respectively). Finally, potential lamp sets are formed by grouping the extracted components on the basis of their positions, sizes, and colours. The potential extracted vehicles are classified as a vehicle or non-vehicle by using a pre-trained CNN (Convolutional Neural Network) classifier. The proposed system was tested and evaluated using different works from the literature. The experiments show that our proposed system has reached a high accuracy in terms of vehicle detection process whether in day or night time. The experiments were performed using four different videos and were implemented using the C++ language, which facilitates mathematical computation, and its OpenCV library, which is used to run the image processing algorithms used, as well as the TensorFlow library, which facilitates transfer learning of pre-trained CNN models.

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A Traffic Surveillance System in Real-Time to Detect and Classify Vehicles by Using Convolutional Neural Network

Cited by 4 publications

References 12 publications

New Directions in Traffic Control Analysis through Video Surveillance

New Directions in Traffic Control Analysis through Video Surveillance

Deep Learning for Facial Beauty Prediction

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

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