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Abstract
The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR) of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate.
Highlights
Traffic accident data indicates that nighttime accidents are more hazardous than those during the daytime [1], and that accidents caused by the rear-end collisions account for more than a third of all the accidents during nighttime [2]
The rules of extracting and pairing taillights in the existing vehicle detection algorithm are usually limited by fixed thresholds, which cannot be adapted to real traffic scenes
We study the problem of detecting the preceding vehicles in nighttime traffic scenes via the image processing technique
Summary
Traffic accident data indicates that nighttime accidents are more hazardous than those during the daytime [1], and that accidents caused by the rear-end collisions account for more than a third of all the accidents during nighttime [2]. This method could improve the detection rate effectively, but the extraction rule is fixed in essence and the inter-frame information is not fully used Another aspect is adding a tracking algorithm to taillights detection to use the inter-frame information. Feature thresholds are adapted based on the similar features of the bright spots in the PR in the previous frame This is another contribution of the paper since the detection rate is improved. The remaining parts are organized as follows: Section 2 introduces the general vehicle detection process based on the taillight pairs in the global image.
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