<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Traffic Monitoring System for Vehicle Detection in Day and Night Conditions"}

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...

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

Pubblicato online: 17 giu 2023

Pagine: 256 - 265

DOI: https://doi.org/10.2478/ttj-2023-0020

Parole chiave
Image processing, Traffic surveillance system, Vehicle detection, Day-time detection, Night-time detection, Discrete wavelet transform (DWT), Convolutional neural network (CNN)

© 2023 Ibtissam Slimani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

Pubblicato online: 17 giu 2023

Pagine: 256 - 265

DOI: https://doi.org/10.2478/ttj-2023-0020

Parole chiaveImage processing, Traffic surveillance system, Vehicle detection, Day-time detection, Night-time detection, Discrete wavelet transform (DWT), Convolutional neural network (CNN)

© 2023 Ibtissam Slimani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Image processing, Traffic surveillance system, Vehicle detection, Day-time detection, Night-time detection, Discrete wavelet transform (DWT), Convolutional neural network (CNN)