在基于图像的车辆与行人检测中,针对固定比例/区域的感兴趣区域(region of interest,ROI)图像分割适应性低的问题,提出基于消失点和车辆高度的ROI自适应分割算法。首先,利用车道消失点得出道路位置,避免分割区域浪费;其次,综合车辆实际...在基于图像的车辆与行人检测中,针对固定比例/区域的感兴趣区域(region of interest,ROI)图像分割适应性低的问题,提出基于消失点和车辆高度的ROI自适应分割算法。首先,利用车道消失点得出道路位置,避免分割区域浪费;其次,综合车辆实际高度和检测距离计算图像上车辆高度,定位ROI边界,减少车辆及行人目标的不完整分割;最后,循环利用当前帧的车道消失点及其推导的实时俯仰角更新下一帧ROI,实时适应不断变化的路面坡度及车身俯仰姿态。实验表明,该算法计算简单,适应性强,满足不同情况下快速精确的ROI分割要求,提高后续目标检测的实时性和准确性。展开更多
3D object detection is one of the most challenging research tasks in computer vision. In order to solve the problem of template information dependency of 3D object proposal in the method of 3D object detection based o...3D object detection is one of the most challenging research tasks in computer vision. In order to solve the problem of template information dependency of 3D object proposal in the method of 3D object detection based on 2.5D information, we proposed a 3D object detector based on fusion of vanishing point and prior orientation, which estimates an accurate 3D proposal from 2.5D data, and provides an excellent start point for 3D object classification and localization. The algorithm first calculates three mutually orthogonal vanishing points by the Euler angle principle and projects them into the pixel coordinate system. Then, the top edge of the 2D proposal is sampled by the preset sampling pitch, and the first one vertex is taken. Finally, the remaining seven vertices of the 3D proposal are calculated according to the linear relationship between the three vanishing points and the vertices, and the complete information of the 3D proposal is obtained. The experimental results show that this proposed method improves the Mean Average Precision score by 2.7% based on the Amodal3Det method.展开更多
文摘在基于图像的车辆与行人检测中,针对固定比例/区域的感兴趣区域(region of interest,ROI)图像分割适应性低的问题,提出基于消失点和车辆高度的ROI自适应分割算法。首先,利用车道消失点得出道路位置,避免分割区域浪费;其次,综合车辆实际高度和检测距离计算图像上车辆高度,定位ROI边界,减少车辆及行人目标的不完整分割;最后,循环利用当前帧的车道消失点及其推导的实时俯仰角更新下一帧ROI,实时适应不断变化的路面坡度及车身俯仰姿态。实验表明,该算法计算简单,适应性强,满足不同情况下快速精确的ROI分割要求,提高后续目标检测的实时性和准确性。
基金Supported by the National Natural Science Foundation of China(61772328,61802253,61831018)
文摘3D object detection is one of the most challenging research tasks in computer vision. In order to solve the problem of template information dependency of 3D object proposal in the method of 3D object detection based on 2.5D information, we proposed a 3D object detector based on fusion of vanishing point and prior orientation, which estimates an accurate 3D proposal from 2.5D data, and provides an excellent start point for 3D object classification and localization. The algorithm first calculates three mutually orthogonal vanishing points by the Euler angle principle and projects them into the pixel coordinate system. Then, the top edge of the 2D proposal is sampled by the preset sampling pitch, and the first one vertex is taken. Finally, the remaining seven vertices of the 3D proposal are calculated according to the linear relationship between the three vanishing points and the vertices, and the complete information of the 3D proposal is obtained. The experimental results show that this proposed method improves the Mean Average Precision score by 2.7% based on the Amodal3Det method.
