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.

Human Action Recognition (HAR) is an important task in computer vision with diverse applications. However, most existing methods rely on all frames of an action video for classification, which leads to high computational cost and low efficiency. In many cases, a compact set of key keyframes can effectively encode the essence of a complete action. Therefore, this study proposes an efficient HAR method that combines a new keyframe extraction algorithm with a lightweight neural network. Our contribution is three-fold. Firstly, an accurate and efficient key frame algorithm is proposed to alleviate the issue of frame-order confusion in classical clustering methods. Secondly, a key-frame-based multi-feature fusion matrix is constructed to address information loss from spatio-temporal trajectory overlap and the sensitivity issue of viewpoint changes in classical models. Thirdly, a lightweight neural network model is designed to achieve effective convergence within a short training period. The proposed method was evaluated on two public datasets (UTKinect-Action3D and Florence-3D) and a self-collected dataset (HanYue-3D). The experiment results show the advantages of our method in both accuracy and efficiency.