Informatica

Calculating motion features frame by frame and organizing them into a 3D matrix is a typical CNN-based solution for human action recognition (HAR). With the widespread use of consumer electronics, reducing computational costs and enabling efficient edge-side action recognition have become a research hotspot. In this paper, we extract action key frames via a well-designed algorithm to reduce computational overhead, so that the proposed method can be deployed on mobile electronic devices. Then we construct local and global motion features from these key frames and feed them into a cascade neural network for action recognition. The primary contributions include three aspects. First, the strategic adoption of key frames is introduced to greatly reduce the number of input parameters. The number of key frames can be adjusted to adapt to the temporal scales of different actions. Second, multiple origin points are adopted to construct motion matrices with larger dimensions than those constructed using a single origin point. Thus, deeper neural networks can be employed to achieve higher recognition accuracy. Third, a cascade neural network is proposed for action prediction, which leverages global and local information to achieve better efficiency and accuracy. Experimental results on UTKinect-Action3D, Florence-3D and our self-built HanYue-3D datasets demonstrate that our method achieves accuracy and efficiency competitive with state-of-the-art (SOTA) approaches. Moreover, the flexibility of the proposed method enables users to readily balance effectiveness and efficiency, making it well-suited for resource-constrained mobile devices.