Nighttime vehicle detection and classification via headlights trajectories matching

Vu, Tuan-Anh; Pham, Long Hoang; Huynh, Tu Kha; Ha, Synh Viet-Uyen

doi:10.1109/icsse.2017.8030869

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Similarly, the authors of (Vu et al, 2017) propose a night vehicle detection as well as classification system for traffic monitoring. Their method includes headlamp segmentation, headlamp recognition, headlamp tracking and matching, and vehicles classification.…”

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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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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“…The brightness and appearance of strong reflections are similar to those of vehicle lamps; they are collectively called bright objects. To distinguish vehicle lamps from bright objects, many studies [16–20] have used threshold methods [21, 22] to detect all bright objects in a scene and have then filtered out the reflections using a rule‐based procedure. In practice, the threshold methods are combined with morphological operations and aspect ratios to obtain good results.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [18] used the similarity and symmetry of lamps for pairing but these characteristics change with the camera viewing angle and vehicle location. Vu et al [22] used the area ratios of lamps for preliminary pairing and then tracked the vehicle lamps using the vertical and horizontal coordinates of images. Although this method is simple, the area ratio must rely on a perfect vehicle lamp detection result; in reality, this result is unreliable.…”

Section: Introductionmentioning

confidence: 99%

High‐accuracy vehicle lamp detection for real‐time night‐time traffic surveillance

Tsai

Chen

2020

IET intell. transp. syst

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Vehicle lamps are an important image feature of the night‐time vehicle detection algorithm. This study proposes a real‐time night‐time vehicle detection algorithm based on light attenuation characteristic analysis, which consists of vehicle lamp detection and pairing. For the detection phase, this study proposes an automatic dual‐threshold method to quickly extract the attenuation regions around bright objects. This method is highly adaptable and can accurately extract attenuation regions to identify vehicle lamps and strong reflections. For the pairing phase, this study uses the moving trajectories of vehicle lamps to complete preliminary pairing. Then, the vehicle lamp pairing is optimised using the relative vertical and horizontal lamp positions in continuous images. Pixel‐ and object‐based methods were used to verify the experimental vehicle lamp detection results. The results of both verification methods indicate that the proposed algorithm without morphological operation is superior to other algorithms. The accuracy of the pairing phase was >94.6% in scenes with multiple lamps, high‐driving speed, high traffic flow, and rain. Finally, the proposed night‐time vehicle detection algorithm can achieve a real‐time execution speed for images 960 × 720 pixels in size.

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“…Vu, T. A. et al[24] suggested a system for vehicle identification and recognition at night. This strategy consists of segmentation of headlights, identification of headlights, headlight tracking, headlight pairing and classification of vehicles.…”

mentioning

confidence: 99%

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

Parvin¹,

Rozario²,

Islam³

2021

JCC

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An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.

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Nighttime vehicle detection and classification via headlights trajectories matching

Cited by 10 publications

References 17 publications

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

Traffic Monitoring System for Vehicle Detection in Day and Night Conditions

High‐accuracy vehicle lamp detection for real‐time night‐time traffic surveillance

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

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