Arbitrary Interactive Object Detection Method Based on Deep Normalization
Detection and recognition of interactive objects is a key technology to realize human-computer interaction,and in order to solve the problem of limited types of interactive objects in the process of human-computer interaction,an arbitrary interactive object detection method was proposed based on image segmentation.Firstly,for the depth image,after filtering out the data outside the range of the original depth data,the min-max scale normalization method was used to improve the quality of the depth image.Secondly,the target area was segmented by using the image processing method based on saliency detection and the human pose-guided region growth algorithm for the operator's side-to-side camera and front-facing camera posture,respectively.Then,the pixel set of the target object obtained by the above segmentation was input into the image processing functions,and the minimum external rectangle of the area point set was obtained,and the rotating bounding box of the target object was anchored.Then,for the depth image,after filtering out the data outside the range of the original depth data,the min-max scale normalization method was used to improve the quality of the depth image.Finally,the detection experiments of arbitrary interactive objects and the ranging and following experiments of different depth intervals were carried out.Experimental results showed that the proposed object detection method had a lower detection cost and a higher degree of freedom in the detection category of interactive objects,which can realize the detection of arbitrary interactive objects,and had wide applicability in the detection of interactive objects.The normalization of the small depth interval can effectively improve the depth image quality,make the object position error smaller,and improve the accuracy of the object detection distance and the following effect of the robot in the human-computer interaction experiment.
万方数据
维普
