A Novel SURF Based on a Unified Model of Appearance and Motion-Variation

The speeded-up robust features (SURFs) algorithm is the best and most efficient local invariant feature algorithm for application to 2-D images and is widely applied in the fields of 2-D image processing and computer vision. Compared to 2-D images, a video has motion information in addition to its appearance information. Here, to make full use of the video appearance and motion information, we use geometric algebra as the mathematical calculation and analysis framework to obtain the embedded appearance and motion information on a local area of a video. In our proposed model of appearance and motion variation (UMAMV), we developed SURF feature detection and description algorithms operating on the spatio-temporal domain with video appearance and motion information. First of all, a model of appearance and motion variation, which contains video appearance and motion information in the framework of geometric algebra, is proposed. Then, based on this model, we propose a novel detection algorithm, theUMAMV-SURF detector, which mainly contains Hessian matrix construction, Hessian matrix determinant approximation calculation, and non-maximal suppression determination feature points as its key steps. Then, we introduce the UMAMV-SURF description algorithm, which mainly includes determining the dominant orientation of UMAMV-SURF feature points and generating the UMAMV-SURF feature descriptors. Finally, by experimenting with the Weizman and UCF101 datasets, the experimental results show that the proposed UMAMV-SURF algorithm can detect those SURF feature points which can have unique appearance information in the spatial domain and reflect motion change in the temporal domain. Moreover, it offers a higher accuracy than other spatio-temporal interest point algorithms in human behavior recognition of video footage.