基于视觉图像的震区人员搜救方法研究

传统基于信号传感技术的震区人员搜救方法，利用惯性导航传感技术融合移动距离和方向角对待搜救人员进行定位，容易受到天气状况不理想以及局部遮挡的影响，其搜救效率和精度低。提出基于视觉图像的震区人员搜救方法，利用图像采集设备采集震区初始视觉图像，采用小波降噪法降噪处理，提升可辨程度。通过色彩对比方法提取降噪后震区人员视觉图像的特征，并与震区原始图像特征对比，获取震区待搜救人员候选图像；根据候选图像分别采用Kalman滤波跟踪算法和Mean shift跟踪算法，在天气状况不理想以及局部遮挡的复杂震区环境中跟踪搜救人员目标。实验结果表明，所提方法查全率保持在98.5%以上，平均准确率约为98%，人员搜救平均时间约为23 s，说明所提方法能够进行高效、准确的震区人员搜救。

Research on Personnel Search-and-rescue Method in Earthquake Area Based on Visual Imagery

Traditionally, inertial navigation sensor technology, based on signal sensing technology, has been used to locate search-and-rescue personnel by combining the distance they have moved with their angle of direction. This method is easily affected by poor weather conditions and partial occlusion, and the resulting search-and-rescue efficiency and accuracy are low. In this paper, we propose a new search-and-rescue method based on visual imagery for victims in earthquake areas. Initial visual images of the seismic area are collected by image acquisition equipment, and the wavelet denoising method is used to reduce the noise and improve the discernibility of the images. Then, after noise reduction, the visual image features of people in the seismic area are extracted using the color contrast method, and compared with the original image features of the seismic area, to obtain candidate images of rescuers in the seismic area. According to these candidate images, a Kalman-filter tracking algorithm and a mean-shift tracking algorithm are used to track search-and-rescue personnel in complicated seismic areas during poor weather conditions and partial occlusion, respectively. The experimental results show that the recall rate of the proposed method is above 98.5%, the average accuracy is about 98%, and the average search-and-rescue time is about 23 s. These results indicate that the proposed method can efficiently and accurately locate victims for search-and-rescue operations in seismic areas.