基于自适应伽马校正的异常驾驶行为检测方法

doi:10.19678/j.issn.1000-3428.0065832

摘要/Abstract

摘要：

针对低照度条件下重型卡车司机异常驾驶行为检测方法存在检测准确率低、检测速度慢等问题，结合图像自适应增强方法和轮廓定位检测思想，提出一种基于自适应伽马校正的异常驾驶行为检测方法。对传入视频图像进行自适应伽马校正，通过抑制噪声、改善暗部和提升信息熵来提高识别准确率。基于图像灰度值和信息熵对双阈值伽马函数进行自适应调节，从而获得更丰富的边缘信息和色彩信息。利用K-近邻背景建模法将驾驶员前景图像分离以确定检测区域，通过边缘检测进行驾驶员头部和手部轮廓识别，获得关键定位点间的欧氏距离，并进行异常驾驶行为判断。在此基础上，结合异常行为次数和时间阈值，解决环境干扰和图像噪声的问题。实验结果表明，与单尺度Retinex、多尺度Retinex以及自适应直方图均衡方法相比，所提方法在提高检测准确率的同时有效提高了检测速度，能在不同环境下有效检测异常驾驶行为。

关键词: 异常驾驶检测, 关键点定位, 伽马校正, 边缘检测, K-近邻背景建模

Abstract:

To address the problems of low detection accuracy and slow detection speed of heavy truck driver abnormal driving behavior detection methods under low illumination conditions, an abnormal driving behaviors detection method based on adaptive Gamma correction is proposed by combining image adaptive enhancement methods and contour localization detection ideas.The adaptive Gamma correction is applied to the incoming video image to realize noise suppression, darkness improvement, and information entropy enhancement to improve the accuracy of recognition. The double threshold Gamma function is adaptively adjusted based on the image grey value and information entropy to obtain richer edge information and color information.A K-Nearest Neighbor(KNN) background subtraction model is constructed to separate the driver foreground image to determine the detection area, while the driver head and hand contours are identified by edge detection to obtain the Euclidean distance between key localization points for abnormal driving behavior judgement. Based on this, the number of abnormal behaviors and time thresholds are fused to solve the problem of environmental interference and image noise. The experimental results show that when compared with the Single-Scale Retinex(SSR), Multi-Scale Retinex(MSR) and the adaptive histogram equalization method, the proposed method not only improves detection accuracy but also effectively improves detection speed. Furthermore, the proposed method can effectively detect abnormal driving behavior in different environments.

Key words: abnormal driving detection, key points location, Gamma correction, edge detection, K-Nearest Neighbor(KNN) background modeling

艾青松, 张皓喆, 严俊伟. 基于自适应伽马校正的异常驾驶行为检测方法[J]. 计算机工程, 2023, 49(9): 279-286.

Qingsong AI, Haozhe ZHANG, Junwei YAN. Abnormal Driving Behavior Detection Method Based on Adaptive Gamma Correction[J]. Computer Engineering, 2023, 49(9): 279-286.

https://www.ecice06.com/CN/Y2023/V49/I9/279

图/表 10

图1 不安全驾驶行为示例

Fig.1 Examples of unsafe driving behavior

图2 基于自适应伽马校正的异常驾驶行为检测方法框架

Fig.2 Framework of abnormal driving behavior detection method based on adaptive Gamma correction

图3 改进伽马校正的流程

Fig.3 Procedure of improving Gamma correction

图4 坐标系转换

Fig.4 Coordinate system transformation

图5 图像预处理前后的对比

Fig.5 Comparison of the graph before and after preprocessing

图6 不同参数下的伽马校正效果

Fig.6 Gamma correction effect under different parameters

图7 暗光条件下不同增强方法的性能对比

Fig.7 Performance comparison of different enhancement methods in dark conditions

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