Preceding vehicle detection using Histograms of Oriented Gradients

2010 ◽  
Author(s):  
Ling Mao ◽  
Mei Xie ◽  
Yi Huang ◽  
Yuefei Zhang
Keyword(s):  
Vehicle Detection ◽  
Histograms Of Oriented Gradients ◽  
Preceding Vehicle

scholarly journals Preceding Vehicle Detection Based on Optimized Faster R-CNN Algorithm

2021 ◽  
Vol 1802 (3) ◽  
pp. 032075
Author(s):  
Yongqing Wang ◽  
Guochen Cui ◽  
Shufeng Wang ◽  
Junyou Zhang
Keyword(s):  
Vehicle Detection ◽  
Preceding Vehicle

scholarly journals Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1024 ◽  
Author(s):  
Cui ◽  
Wang ◽  
Wang ◽  
Liu ◽  
Yuan ◽  
...  
Keyword(s):  
Experimental Validation ◽  
Autonomous Vehicle ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
Vehicle Speed ◽  
Challenging Problem ◽  
Penalty Coefficient ◽  
Preceding Vehicle ◽  
Vehicle Technologies ◽  
Vehicle Size

At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy.

Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification

Optik ◽  
2016 ◽  
Vol 127 (19) ◽  
pp. 7941-7951 ◽  
Author(s):  
Gang Yan ◽  
Ming Yu ◽  
Yang Yu ◽  
Longfei Fan
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Histograms Of Oriented Gradients

scholarly journals Visual Meterstick: Preceding Vehicle Ranging Using Monocular Vision Based on the Fitting Method

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1081
Author(s):  
Chaochao Meng ◽  
Hong Bao ◽  
Yan Ma ◽  
Xinkai Xu ◽  
Yuqing Li
Keyword(s):  
Vehicle Detection ◽  
Semantic Segmentation ◽  
Safe Driving ◽  
Collision Warning ◽  
Bounding Box ◽  
Fitting Method ◽  
Measurement Results ◽  
Ranging Error ◽  
Preceding Vehicle ◽  
Pixel Value

The gradual application of deep learning in the field of computer vision and image processing has made great breakthroughs. Applications such as object detection, recognition and image semantic segmentation have been improved. In this study, to measure the distance of the vehicle ahead, a preceding vehicle ranging system based on fitting method was designed. First obtaining an accurate bounding box frame in the vehicle detection, the Mask R-CNN (region-convolutional neural networks) algorithm was improved and tested in the BDD100K (Berkeley deep derive) asymmetry dataset. This method can shorten vehicle detection time by 33% without reducing the accuracy. Then, according to the pixel value of the bounding box in the image, the fitting method was applied to the vehicle monocular camera for ranging. Experimental results demonstrate that the method can measure the distance of the preceding vehicle effectively, with a ranging error of less than 10%. The accuracy of the measurement results meets the requirements of collision warning for safe driving.

Integrated Real-Time Vision-Based Preceding Vehicle Detection in Urban Roads

2011 ◽  
pp. 270-275 ◽  
Author(s):  
Yanwen Chong ◽  
Wu Chen ◽  
Zhilin Li ◽  
William H. K. Lam ◽  
Qingquan Li
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Preceding Vehicle

Integrated real-time vision-based preceding vehicle detection in urban roads

2013 ◽  
Vol 116 ◽  
pp. 144-149 ◽  
Author(s):  
Yanwen Chong ◽  
Wu Chen ◽  
Zhilin Li ◽  
William H.K. Lam ◽  
Chunhou Zheng ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Preceding Vehicle

Preceding Vehicle Detection Based on Multi-characteristics Fusion

2006 ◽  
Author(s):  
Jin Li-sheng ◽  
Gu Bai-yuan ◽  
Wang Rong-ben ◽  
Guo lie ◽  
Zhao Yi-bing ◽  
...  
Keyword(s):  
Vehicle Detection ◽  
Preceding Vehicle
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