An image based overexposed taillight detection method for frontal vehicle detection in night vision

Chien, Chia Hung; Hang, Hsueh-Ming; Tseng, Din-Chang; Chen, Yongsheng

doi:10.1109/apsipa.2016.7820881

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Due to the complex nighttime environment, the traditional method using the similarity of light shapes will lead to false detection [ 27 ]. Since the HOG feature has the advantage of robustness to lighting conditions, invariance to geometric transformations, effectiveness in cluttered backgrounds, dimensionality reduction, compatibility with machine learning algorithms, high detection accuracy, and adaptability to real-time applications [ 28 ], it is often used in vehicle detection. However, due to the time-consuming and low noise immunity of the HOG feature and the lack of vehicle contour pieces of information at night, the effect of using contour features for classification does not perform as well as in daytime [ 22 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

Zhang,

Xu,

Shen

et al. 2024

Sensors

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The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.

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Section: Proposed Methodsmentioning

confidence: 99%

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

Zhang,

Xu,

Shen

et al. 2024

Sensors

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show abstract

“…Information-Based Methods. The color information-based methods often extract features via morphology [23,24] and color/intensity thresholds [25][26][27]. Different color spaces are used, such as RGB [28], HSV [29], YCbCr [27], YUV [30], and Lab [31].…”

Section: Colormentioning

confidence: 99%

Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor

Liu

Chen

et al. 2022

Journal of Advanced Transportation

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An important aspect of the perception system for intelligent vehicles is the detection and signal measurement of vehicle taillights. In this work, we present a novel vision-based measurement (VBM) system, using an event-based neuromorphic vision sensor, which is able to detect and measure the vehicle taillight signal robustly. To the best of our knowledge, it is for the first time the neuromorphic vision sensor is paid attention to for utilizing in the field of vehicle taillight signal measurement. The event-based neuromorphic vision sensor is a bioinspired sensor that records pixel-level intensity changes, called events, as well as the whole picture of the scene. The events naturally respond to illumination changes (such as the ON and OFF state of taillights) in the scene with very low latency. Moreover, the property of a higher dynamic range increases the sensor sensitivity and performance in poor lighting conditions. In this paper, we consider an event-driven solution to measure vehicle taillight signals. In contrast to most existing work that relies purely on standard frame-based cameras for the taillight signal measurement, the presented mixed event/frame system extracts the frequency domain features from the spatial and temporal signal of each taillight region and measures the taillight signal by combining the active-pixel sensor (APS) frames and dynamic vision sensor (DVS) events. A thresholding algorithm and a learned classifier are proposed to jointly achieve the brake-light and turn-light signal measurement. Experiments with real traffic scenes demonstrate the performance of measuring taillight signals under different traffic conditions with a single event-based neuromorphic vision sensor. The results show the high potential of the event-based neuromorphic vision sensor being used for optical signal measurement applications, especially in dynamic environments.

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“…For obtaining ROIs of vehicles, the following techniques can be adopted: threshold-based segmentation methods [5], [12], [18], paired vehicle lighting-based methods [6]- [8], [14]- [16], saliency map-based methods [17], [27], and artificially designed feature-based methods [13]. After the ROIs are obtained, we need to further determine whether these candidate regions contain vehicles.…”

Section: Related Work a Nighttime Vehicle Detectionmentioning

confidence: 99%

Feature Enhancement Based on CycleGAN for Nighttime Vehicle Detection

et al. 2021

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An image based overexposed taillight detection method for frontal vehicle detection in night vision

Cited by 7 publications

References 11 publications

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features

Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor

Feature Enhancement Based on CycleGAN for Nighttime Vehicle Detection

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