We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: the non-maximum suppression, the soft non-maximum suppression and the soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.
翻译:我们提议在万向图像上设置个人探测器,这是产生最小随附矩形的精确方法。基本想法是使以远视图像为基础的神经网络方法(即YOLOv2)的质量检测性能适应鱼眼图像。我们的方法设计采用了最先进的天体探测器和与其利益区域高度重叠的图像区域的概念。这种重叠减少了假底片的数量。根据探测器的原始捆绑盒,我们通过三种方法微调调整重叠捆绑箱:非最大抑制、软非最大抑制和以高斯平滑的软非最大抑制。评价是在PIROPO数据库和自己的附加说明的Flat数据集上进行的,并辅之以全向图像的捆绑框。我们实现了平均精确度64.4%的YOLOv2,PIROPO的班级和77.6%的平板。为此,我们用高斯平滑的压调整了软非最高压制。