Nighttime Vehicle Detection at Close Range Using Vehicle Lamps Information

Wu, Jun; Lee, Jiann Der; Chien, Jong Chih; Hsieh, Chu‐Chin

doi:10.1109/is3c.2014.320

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…D A ′ B ′ and D C ′ D ′ are the pixel widths of the road in the image. Therefore, we can obtain Equation (8).…”

Section: Low Probability Candidate Filtermentioning

confidence: 99%

“…Currently, vehicle detection is mostly based on visual images [3][4][5][6][7][8][9][10]. The visual image is not clear at night, and the detail of the vehicle is also unclear.…”

Section: Introductionmentioning

confidence: 99%

“…The visual image is not clear at night, and the detail of the vehicle is also unclear. In order to overcome the problem, a number of papers have been published to detect vehicles at night by identifying the shape and track of the headlights [3][4][5][6][7][8][9][10]. Many studies were both detecting vehicles via headlights pairing and trajectories matching [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…The dominant color components in Red Green Blue (RGB) images were then processed by the threshold to extract blobs for the headlight [7]. However, this nighttime vehicle detection method depended on clear headlights or taillights shape [5,[8][9][10], and the existence of high beams glares was ignored. When the vehicle headlight was captured by a camera, it could produce a halo, which would affect the judgment and measurement of the vehicle headlight.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Adjustment and Distinguishing Method for Vehicle Headlight Based on Data Access of a Thermal Camera

Bai

Qin

2020

Front. Phys.

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In recent years, more and more traffic accidents have been caused by the illegal use of high beams. Therefore, the distinguishing of the vehicle headlight is vital for night driving and traffic supervision. Then, a method for distinguishing vehicle headlight based on data access of a thermal camera was proposed in this paper. There are two steps in this method. The first step is thermal image dynamic adjustment. In thermal image dynamic adjustment, the details of thermal images were enhanced by adjusting the temperature display dynamically and fusing features of multi-sequence images. The second step is vehicle headlight dynamic distinguishing, and features of vehicle headlight were extracted by YOLOv3. Then, the high beam and low beam were further distinguished by the filter based on the position and proportion relationship between the halo and the beam size of vehicle headlights. In addition, the accessed thermal image dataset during the night was used for training purposes. The results showed that the precision of this method was 94.2%, and the recall was 78.7% at a real-time speed of 9 frames per second (FPS). Compared with YOLOv3 on the Red Green Blue (RGB) image, the precision was further improved by 11.1%, and the recall was further improved by 5.1%. Dynamic adjustment and distinguishing method were also applied in single-shot multibox detector (SSD) network which has good performance in small-object detection. Compared with the SSD network on the RGB image, the precision was improved by 8.2% and the recall was improved by 4.6% when SSD network was improved by this method.

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“…D A ′ B ′ and D C ′ D ′ are the pixel widths of the road in the image. Therefore, we can obtain Equation (8).…”

Section: Low Probability Candidate Filtermentioning

confidence: 99%

“…Currently, vehicle detection is mostly based on visual images [3][4][5][6][7][8][9][10]. The visual image is not clear at night, and the detail of the vehicle is also unclear.…”

Section: Introductionmentioning

confidence: 99%