A Human Behavior Recognition Method Based on 3D Convolutional Neural Network and Transfer Learning Model

Abstract

The invention relates to a human behavior recognition method based on a three-dimensional convolutional neural network and a migration learning model, including: sampling a video frame by frame, and stacking the obtained multiple continuous single-frame images to a certain size in the time dimension The image cube of , as the input of the 3D neural network. When implementing, first train the basic multi-category 3D neural network model, then select some input samples from the test results to construct a sub-dataset, and then train multiple binary classification models on the basis of this sub-dataset, and select two Several models with the best classification results. Finally, the knowledge learned by these models is transferred to the original multi-classification model by using transfer learning, and then the transferred multi-classification model is retrained to improve the accuracy of multi-classification recognition and achieve high-accuracy human behavior recognition.

Description

technical field [0001] The invention relates to image processing technology in video, in particular to a human behavior recognition method based on a three-dimensional convolutional neural network and a migration learning model. Background technique [0002] In today's society, with the rapid development of storage devices, Internet technology and social networks, large-scale video data has been generated. How to use these video data for target recognition and behavior analysis has become a growing demand. Whether it is intelligent security monitoring, customer shopping behavior analysis, smart home systems, somatosensory games, or the action recognition of pedestrians on the road during unmanned driving, they all rely on high-efficiency and high-precision human behavior recognition systems. The purpose of human behavior recognition is to classify and recognize the behavior or action of one or more people in the video. The research object is often a series of video sequence...

AI Technical Summary

Problems solved by technology

Considering the distribution of behavior in time and space dimensions, different individuals may have different performances for the same type or type of action. These differences are often caused by different individuals' personal understanding of the same type or type of action. Therefore, there are often large differences in the integrity, motion trajectory, motion range, and motion speed of the same action, resulting in difficulties in distinguishing; (2) There are often many interference information in dynamic video information, and many of these information are in the process of static image processing. did not appear in

For example, occlusion between individuals, between individuals and the background environment, relative motion between individuals or between individuals and the background environment during the occlusion process, changes in light intensity and contrast in the video sequence, video The movement and zoom of the lens during shooting, and the action in a video sequence may be incomplete, etc.

All these difficulties have brought great challenges to the research of behavior recognition in video sequences.

Images