Review of Human Behavior Recognition Research

Abstract

Behavior recognition is a hot topic in the field of computer vision. It has experienced the development process from manual design feature representation to deep learning feature expression. This paper classifies the mainstream algorithms in the development of behavior recognition from two aspects of traditional behavior recognition models and deep learning models. The traditional behavior recognition models mainly include feature description methods based on silhouette, space-time interest points, human joint point and trajectories. Among them, the improved dense trajectory method has good robustness and reliability. Deep learning network architecture mainly includes two-stream network, 3D convolution network and hybrid network. Firstly, this paper focuses on the main research ideas and innovations of each behavior recognition algorithm, and introducees the model architecture, algorithm features, application scenarios of each kind of algorithm. Then, the widely used public behavior databases are classified, and the HMDB51 and UCF101 datasets are introduced in detail. The recognition effects of traditional methods and deep learning algorithms on each dataset are compared and analyzed. Through comparative analysis, the traditional methods are not suitable for high-precision behavior recognition, and it is not easy to achieve cross database or cross scene promotion. In depth architecture, two-stream network and 3D convolution network have achieved good behavior recognition effect and are widely used. Finally, the future development of behavior recognition is prospected, and some feasible research directions in the future are pointed out.

Keywords

• |human behavior recognition|deep learning|neural network|behavior dataset