Nighttime Brake-Light Detection by Nakagami Imaging

Chen, Duan-Yu; Lin, Yu-Hao; Peng, Yang-Jie

doi:10.1109/tits.2012.2199983

Cited by 50 publications

(37 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To emphasize vehicle lights, nakagami image [17] is computed from color intensity image. Nakagami image is now converted to binary image for locating light regions using counter detection.…”

Section: = Max( ) 255mentioning

confidence: 99%

Night-time Vehicle Detection for Automatic Headlight Beam Control

Sevekar¹,

Dhonde²

2017

IJCA

View full text Add to dashboard Cite

A clear visibility of the road ahead is significant concern for safe nighttime driving. However, high beams are utilized less than sufficient on the roads since drivers are afraid of dazzling others. Subsequently, the smart programmed control of vehicles front lamp is of incredible significance.According to road accident surveys, majority of the accidents occur in dark. Visibility in dark is significant issue for safe driving. Therefore careless drivers continue using a high beam even though approaching vehicle is observed. These high beams create glare for approaching drivers which then causes temporary blindness. To solve this problem, nighttime vehicle detection has a great importance. This paper reviews various attempts made to solve the problem, need of study, currently implemented relevant systems and related work, different approaches to solve problem and various applications. The survey shows sensor based approaches cannot be used to satisfy real time requirements of a system. However, a simple and reliable solution needs to be developed so that, it can be implemented in each vehicle. An attempt has been made to present an image processing based system that detects vehicles and selects ideal beam so that accidents due to temporary blindness can be reduced. General TermsLight blob detection, Support vector machines.

show abstract

Section: = Max( ) 255mentioning

confidence: 99%

Night-time Vehicle Detection for Automatic Headlight Beam Control

Sevekar¹,

Dhonde²

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…We implement experiments on the same dataset and give the comparison of recall, precision and time cost between our method and v-b method. The work in [19] is another vision-based work of brake lights detection, the RGB frame is converted to Nakagami image by using a scanning window on the frame and brake-light is detected and 285 marked by using the connected component of the taillight region, we implement this work for the precision of brake-light detection used the parameters set as the author demonstrated. The overall algorithm of our method is described in algorithm 1, which includes two stages.…”

Section: Outputmentioning

confidence: 99%

“…Many other works on vehicle detection are based on the vehicle lights detection in the nighttime [2,3,9,10,4,5,19]. In these methods, the color space are used for segmenting the light regions with threshold, such as HSV color space [2], RGB color space [20], and YCrCb color space [12].…”

mentioning

confidence: 99%

“…Finally we collect 53 vehicles' video clips in different distance, containing more than 30,000 consecutive frames in total. We calculate the recall and precision of brake-light regions detection between our method and other methods, such as vision-based method [12] and Nakagami method [19]. The…”

mentioning

confidence: 99%

“…At the same time, three threshold such as overlap ratio of two bright regions are set which may not be robustness in different environment. The work in[19] is another vision-based work of brake lights detection at nighttime. In this work, some characteristics of the front vehicle 105 such as the symmetry, light position and the light size are not used for brake light detection, instead, they proposed an approach based-on the Nakagami-m distribution for focusing on finding the invariant features to model brake light scattering property to detect the brake-light.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Vision-based two-step brake detection method for vehicle collision avoidance

et al. 2016

View full text Add to dashboard Cite

Deep Hierarchical Rear‐Lamp Tracking at Nighttime

Sun,

Chen,

Zhang

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Rear‐lamp tracking at night is a research topic in night vision that is essential for Advanced Driver Assistance Systems (ADAS). Most current computer vision‐based methods address this problem using color features because rear lamps are lit during night driving. However, such methods are sufficiently robust in complex environments owing to the lack of feature diversity. On the other hand, as there is no off‐the‐shelf dataset, the application of deep learning to nighttime rear‐lamp tracking has been sparsely explored. To tackle the above issues, in this paper, we propose hierarchical rear‐lamp tracking at nighttime (H‐RTN) and create a novel dataset for rear‐lamp tracking. Specifically, the H‐RTN consists of a rough detection hierarchy (R‐hierarchy), an accurate detection hierarchy (A‐hierarchy), and an optimization hierarchy (O‐hierarchy). The R‐hierarchy determines the region of interest (ROI) containing the target rear lamps, the A‐hierarchy samples the rear‐lamp candidates in the ROI, and the O‐hierarchy selects the best pair of candidates as the final location of the target rear lamps. The experimental results show that H‐RTN outperforms the alternative existing methods. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

show abstract

Nighttime Brake-Light Detection by Nakagami Imaging

Cited by 50 publications

References 16 publications

Night-time Vehicle Detection for Automatic Headlight Beam Control

Night-time Vehicle Detection for Automatic Headlight Beam Control

Vision-based two-step brake detection method for vehicle collision avoidance

Deep Hierarchical Rear‐Lamp Tracking at Nighttime

Contact Info

Product

Resources

About