Night-time Vehicle Detection Based on Brake/Tail Light Color

Kavya, Thathupara Subramanyan; Tsogtbaatar, Erdenetuya; Jang, Young-Min; Cho, Sang-Bock

doi:10.1109/isocc.2018.8649981

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…The width lights and brake lights of vehicles are red, therefore the red areas can be detected according to the color-based method discussed above. In [ 83 ], RGB color space was used to extract features of vehicle lights, and then closing operation (one of the morphological operations) was performed to eliminate holes in the feature map. ln [ 84 ] HSV color space was proposed to extract features of car light, after which Gaussian filter was used for filtering and noise reduction, and non-maximum suppression (NMS) method was implemented to eliminate the overlapping area.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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“…In [24], the performance of detectors has been improved by using Multi-model Cascaded and has led to the reduction of false-positives. Kavya et al [25], determined vehicles at night based on the red color of the vehicle's taillights and applied morphological operations.…”

Section: Computer Vision and Deep Learningmentioning

confidence: 99%

Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier

Bagheri,

Barshooi

2023

Preprint

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This paper aims to enhance the ability to predict nighttime driving behavior by identifying the taillights of both human-driven and autonomous vehicles. The proposed model incorporates a customized detector designed to accurately detect front-vehicle taillights on the road. At the beginning of the detector, a learnable pre-processing block is implemented, which extracts deep features from input images and calculates the data rarity for each feature. In the next step, drawing inspiration from soft attention, a weighted binary mask is designed that guides the model to focus more on predetermined regions. This research utilizes Convolutional Neural Networks (CNNs) to extract distinguishing characteristics from these areas, then reduces dimensions using Principal Component Analysis (PCA). Finally, the Support Vector Machine (SVM) is used to predict the behavior of the vehicles. To train and evaluate the model, a large-scale dataset is collected from two types of dash-cams and Insta360 cameras from the rear view of Ford Motor Company vehicles. This dataset includes over 12K frames captured during both daytime and nighttime hours. To address the limited nighttime data, a unique pixel-wise image processing technique is implemented to convert daytime images into realistic night images. The findings from the experiments demonstrate that the proposed methodology can accurately categorize vehicle behavior with 92.14% accuracy, 97.38% specificity, 92.09% sensitivity, 92.10% F1-measure, and 0.895 Cohen’s Kappa Statistic. Further details are available at https://github.com/DeepCar/Taillight_Recognition.

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“…Finally, by pairing the lamps, the vehicle's location is determined. Kavya et al [28] proposed a method for detecting vehicles based on the color of the brake lamp during braking in the captured color image. e feature information required for the above identification method in a reflective environment will detect lamp reflections.…”

Section: Related Workmentioning

confidence: 99%

An Improved VM Obstacle Identification Method for Reflection Road

Jiang

Sang

et al. 2022

Journal of Robotics

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An obstacle detection method based on VM (VIDAR and machine learning joint detection model) is proposed to improve the monocular vision system's identification accuracy. When VIDAR (Vision-IMU-based detection and range method) detects unknown obstacles in a reflective environment, the reflections of the obstacles are identified as obstacles, reducing the accuracy of obstacle identification. We proposed an obstacle detection method called improved VM to avoid this situation. The experimental results demonstrated that the improved VM could identify and eliminate unknown obstacles. Compared with more advanced detection methods, the improved VM obstacle detection method is more accurate. It can detect unknown obstacles in reflection, reflective road environments.

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Night-time Vehicle Detection Based on Brake/Tail Light Color

Cited by 7 publications

References 2 publications

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier

An Improved VM Obstacle Identification Method for Reflection Road

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