Zhang Li, Xu Weiyue, Shen Cong, Huang Yingping
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Changzhou Xingyu Automotive Lighting System Co., Ltd., 182 Qinling Road, Changzhou 213000, China.
Sensors (Basel). 2024 Feb 29;24(5):1590. doi: 10.3390/s24051590.
The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.
在硬件成本受限的情况下，低光照条件下难以识别车辆轮廓特征给夜间车辆检测带来了巨大挑战。为解决这一问题，引入了一种增强型方向梯度直方图（HOG）方法来提取相关车辆特征。首先，通过背景光照去除和显著性模型相结合的方式提取车辆灯光。随后，将这些灯光与基于模板的方法相结合，以划定包含潜在车辆的区域。下一步，在这些区域内进行超像素和HOG（S-HOG）特征融合，并使用支持向量机（SVM）进行分类。应用非极大值抑制（NMS）方法消除重叠区域，其中融合了方向梯度对称特征的垂直直方图（V-HOG）。最后，利用卡尔曼滤波器对候选车辆进行实时跟踪。实验结果表明，所提方法显著提高了夜间场景下车辆识别的准确率。
