Abstract
The current research on still image recognition has been very successful, but the study of action recognition for video classes is still a challenging topic. In this work, we propose a random projection-based human action recognition algorithm to address the lack of depth information in color information (RGB video frames) that is not easily affected by environmental factors such as illumination and the lack of ability to recognize actions along the direction of view. A network structure is designed to take the obvious advantage of long- and short-term memory networks for controlling and remembering long sequences of historical information. The network structure in this paper is constituted by multiple memory units. At the same time, this paper constructs the spatial features, temporal features, and depth features of the three recognition stream outputs into a feature matrix, whose feature matrix is divided into multiple temporal segments according to the temporal dimension, then inputs them into the network layer in order, and achieves the fusion of the feature matrix in this paper according to their correlation characteristics on the temporal axis. Here, we proposed the concept of random batch projection operators. This basically uses as much sublimitation information as possible to improve projection accuracy by randomly selecting several subdependencies as projections defined during projection. A compressed sensing design of human motion acceleration data for low-power body area networks is proposed, and the basic idea and implementation process of compressed sensing theory for human motion data compression and reconstruction in wireless body area networks are introduced in detail.
Highlights
Human behavior action recognition based on the random projection algorithm is an emerging branch in the field of pattern recognition, whose basic idea is to collect the motion signals of human activities through wearable inertial sensors and transmit them to remote data processing centers through wireless communication technology and to classify and recognize them according to some pattern recognition algorithms after feature extraction and selection
The input of the temporal network in this paper is a stacked 20-layer dense optical flow image, so the number of channels of the input features is 20, which is much faster than the number of channels of the temporal network in the previous work [26]
Since the calculation is simple, the same method is adopted in this article. e method of this paper is to generate a two-dimensional projection map in three planes of the depth sequence frame for front view f, side view s, and top view t. e optical flow information input to the global flow network channel is extracted from the RGB video sequence using the tv-l1 optical flow algorithm, and the horizontal and vertical components of the tv-l1 optical flow are adjusted, and the optical flow value is DMM calculation. e depth motion map of the depth action sequence, which effectively depicts the motion characteristics of three orthogonal planes orthogonal to the projection diagram, can be obtained by DMM calculation
Summary
Human behavior action recognition based on the random projection algorithm is an emerging branch in the field of pattern recognition, whose basic idea is to collect the motion signals of human activities through wearable inertial sensors and transmit them to remote data processing centers through wireless communication technology and to classify and recognize them according to some pattern recognition algorithms after feature extraction and selection. With continuous innovations in communication technology and hardware technology, the human behavior recognition has been enhanced by artificial intelligence techniques, such as virtual reality, video analysis, identification, physical interaction, human and machine interaction, intelligent surveillance, medical diagnosis, and so on It has the potential for wide application [6,7,8,9,10]. Compared with the traditional RGB video, the depth video captured by the depth camera is not affected by external environmental factors such as light changes and can be used normally even in dark or no natural light environment, showing significant performance advantages in human action estimation in three-dimensional space and.
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