摘要
航拍图像与普通图像相比,具有以下特殊性:分辨率较低,细节不充分;检测目标体积小,难检测;背景的纹理干扰较大。这些特殊性使得直接应用经典的显著性检测方法在航拍图像上不能取得理想的效果。针对这个问题,根据人类视觉系统的特点,提出了一种通过特征显著性分析确定目标候选区域的车辆检测算法。首先,用车辆底层边条信息对输入图像卷积,得到特征响应图。然后计算不同尺度下的特征响应图的谱残差,得到显著性响应图。最后,通过融合多尺度下的显著性响应图,并进行时域和空域增强处理,得到最终的显著性地图。对真实场景下的图像进行仿真实验,结果表明该算法能取得较好的检测效果。
According to the characteristics of the human visual system,a vehicle detection algorithm based on characteristics saliency analysis is proposed in this paper.First,to obtain a feature response map,the bottom edge of the vehicle information is used to do image convolution on the input image.Then calculate the spectral residual of feature response maps on different scales to obtain saliency response maps.Finally,in order to obtain the final saliency map,multi-scales saliency maps has been merged into one map and temporal enhancement and spatial enhancement method is used.
出处
《工业控制计算机》
2016年第4期75-77,共3页
Industrial Control Computer
关键词
视觉显著性
特征卷积
谱残差
车辆检测
visual saliency
feature convolution
spectral residual
vehicle detection
