Human action recognition is an important part for computers to understand the behavior of people in pictures or videos. In a single image, there is no context information for recognition, so its accuracy still needs to be greatly improved. In this paper, a single-image human action recognition method based on improved ResNet and skeletal keypoints is proposed, and the accuracy is improved by several methods. We improved the backbone network ResNet-50 and CPN to a certain extent and constructed a multitask network to suit the human action recognition task, which not only improves the accuracy but also balances the total number of parameters and solves the problem of large network and slow operation. In this paper, the improvement methods of ResNet-50, CPN, and whole network are tested, respectively. The results show that the single-image human action recognition based on improved ResNet and skeletal keypoints can accurately identify human action in the case of different human movements, different background light, and occlusion. Compared with the original network and the main human action recognition algorithms, the accuracy of our method has its certain advantages.

<abstract> <p>For the needs of tamper-proof detection and copyright identification of audio and video matching, this paper proposes a zero-watermark algorithm that can be used for audio and video matching verification. The algorithm segments audio and video in smaller time units, generates a video frame feature matrix based on NSCT, DCT, and SVD, and generates a sound watermark based on methods such as DWT and K-means. The zero watermark combines video, audio and copyright information. The experimental results show that the zero watermark generated by this algorithm can not only realize highly accurate matching detection and positioning of audio and video, but also well resist common single attack and combination attacks such as noise, scaling, rotation, frame attack and format conversion, which has good robustness.</p> </abstract>

No abstract