Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions

Rezaei, Mahdi; Terauchi, Mutsuhiro; Klette, Reinhard

doi:10.1109/tits.2015.2421482

Cited by 115 publications

(55 citation statements)

References 51 publications

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“…Han et al proposed calculating the distance based on vehicle width estimated by using the lane line and considered the situation without the lane line, but there is a big error in estimating the width of the lane line and target vehicle [30]. Mehdi et al estimated the distance using the height and the pitch angle of the camera by assuming the road is flat, but this method does not consider lateral distance and the influence of camera attitude angles [31]. The above ranging methods have no length reference of a realistic target and rely solely on the imaging principle of the camera, which is challenging to achieve high robustness.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception.

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Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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“…Distance estimation Many prior works for distance estimation mainly focused on building a model to represent the geometry relation between points on images and their corresponding physical distances on the real-world coordinate. One of the classic ways to estimate distance for given object (with a point or a bounding box in the image) was to convert the image point to the corresponding bird's-eye view coordinate using inverse perspective mapping (IPM) algorithm [28,25]. Due to the drawbacks of IPM, it would fail in cases that objects are located over 40 meters apart or on a curved road.…”

Section: Related Workmentioning

confidence: 99%

Learning Object-Specific Distance From a Monocular Image

Zhu

Fang

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving. Many attentions have been paid on the object detection task, but distance estimation only arouse few interests in the computer vision community. Observing that the traditional inverse perspective mapping algorithm performs poorly for objects far away from the camera or on the curved road, in this paper, we address the challenging distance estimation problem by developing the first end-toend learning-based model to directly predict distances for given objects in the images. Besides the introduction of a learning-based base model, we further design an enhanced model with a keypoint regressor, where a projection loss is defined to enforce a better distance estimation, especially for objects close to the camera. To facilitate the research on this task, we construct the extented KITTI and nuScenes (mini) object detection datasets with a distance for each object. Our experiments demonstrate that our proposed methods outperform alternative approaches (e.g., the traditional IPM, SVR) on object-specific distance estimation, particularly for the challenging cases that objects are on a curved road. Moreover, the performance margin implies the effectiveness of our enhanced method.

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“…(1) Feature-based methods Rezaei et al used chain coding to maintain the original accuracy in the process of taillight outline detection and analyze the geometric rules of the taillight outline. They used virtual symmetry detection technology to locate the taillight position [7]. Based on the cognitive theory, Weis et al decomposed a video input stream into color, shape and other features which are closely combined with an image model and created the pixel values of interest in taillight areas according to the characteristics of taillights and atmospheric effects caused by external lighting and weather conditions [8].…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection

Wang

Huo

et al. 2019

Applied Sciences

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Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference.

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Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions

Cited by 115 publications

References 51 publications

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Learning Object-Specific Distance From a Monocular Image

Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection

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