MoVITS: Moroccan Video Intelligent Transport System

Bourja, Omar; Kabbaj, Kaoutar; Derrouz, Hatim; Bouziady, Abderrahim El; Thami, Rachid Oulad Haj; Zennayi, Yahya; Bourzeix, François

doi:10.1109/cist.2018.8596566

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…These applications also impose demands, requiring credible dedicated hardware and reliable and timely communications. In addition, most traffic management systems are based on camera-based video surveillance because of their low cost, ease of maintenance, and ability to capture high quality images of the traffic scene [1]- [4]. This allows the dissemination and collection of useful information between vehicles, and transport infrastructure and vehicles to help drivers travel safely and comfortably.…”

Section: Introductionmentioning

confidence: 99%

“…However, these systems are reliable under normal conditions. In fact, they may not work very well in special circumstances, such as occlusion, bad weather, changes in lighting, and so on [1]- [3], [5]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…In the present study, we are mainly interested in the calculation of inter-vehicle distance, which is an important traffic factor to be studied in intelligent transportation systems [2], [5], [8] [1,5,6]. The aim of this paper is to present the possibility of using stereo cameras instead of LIDAR to estimate the intervehicle distance [1], [5]. Indeed, this method is advantageous because the recordings made by the cameras can be adapted for many algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

Bourja¹,

Derrouz²,

Abdelali³

et al. 2021

IJACSA

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In this paper, we propose a robust real-time vehicle tracking and inter-vehicle distance estimation algorithm based on stereovision. Traffic images are captured by a stereoscopic system installed on the road, and then we detect moving vehicles with the YOLO V3 Deep Neural Network algorithm. Thus, the real-time video goes through an algorithm for stereoscopy-based measurement in order to estimate distance between detected vehicles. However, detecting the real-time objects have always been a challenging task because of occlusion, scale, illumination etc. Thus, many convolutional neural network models based on object detection were developed in recent years. But they cannot be used for real-time object analysis because of slow speed of recognition. The model which is performing excellent currently is the unified object detection model which is You Only Look Once (YOLO). But in our experiment, we have found that despite of having a very good detection precision, YOLO still has some limitations. YOLO processes every image separately even in a continuous video or frames. Because of this much important identification can be lost. So, after the vehicle detection and tracking, inter-vehicle distance estimation is done.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

Bourja¹,

Derrouz²,

Abdelali³

et al. 2021

IJACSA

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“…Congestion is one of the most urging problems in traffic management all over the world, especially for emerging countries. Morocco is developing its first system to manage and control traffic in urban areas [1]. Moroccan video intelligent transport system (MoVITS) contains a set of functionalities including vehicle detection and tracking, vehicle recognition, speed estimation, anomaly detection, and traffic flux analysis.…”

Section: Introductionmentioning

confidence: 99%

Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

Derrouz¹,

Elbouziady²,

Abdelali³

et al. 2019

IEEE Access

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Vehicle type classification is a critical function in any intelligent transportation system (ITS). In this paper, we present a novel two-layer vehicle type classification framework based on the vehicle's 3D parameters and its local features. This framework is a part of the first Moroccan video intelligent transport system (MOVITS) that aims to control traffic and road code violations. In the first layer, the 3D features are extracted using the disparity map generated from stereo-images, and then, the width, height, and length of the vehicle are calculated based on the obtained list of 3D points. In the second layer, a gradient-based method is applied to extract the 2D features, and a dimensional reduction algorithm is performed to reduce its size. Both features are combined to construct the final feature vector that is used as an input for the classification. The Moroccan dataset and the BIT dataset were used to, respectively, validate the proposed framework and conduct a comparative study with the state-of-the-art algorithms. The experimental results demonstrate the efficiency of our approach against existing algorithms.

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MoVITS: Moroccan Video Intelligent Transport System

Cited by 2 publications

References 23 publications

Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

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