Human behavior identification method based on 3D deep convolutional network

Abstract

The invention belongs to the field of computer vision video motion identification, and discloses a human behavior identification method based on a 3D deep convolutional network. The human behavior identification method comprises the steps of: firstly, dividing a video into a series of consecutive video segments; then, inputting the consecutive video segments into a 3D neural network formed by a convolutional computation layer and a space-time pyramid pooling layer to obtain features of the consecutive video segments; and then calculating global video features by means of a long and short memory model, and regarding the global video features as a behavior pattern. The human behavior identification method has obvious advantages, can perform feature extraction on video segments of arbitrary resolution and time length by improving a standard 3D convolutional network C3D and introducing multistage pooling, improves the great robustness of the model to behavior change, is conductive to increasing video training data scale while maintaining video quality, and improves the integrity of behavior information through carrying out correlation information embedding according to motion sub-states.

Description

technical field [0001] The invention belongs to the field of computer vision video recognition, in particular to a method for human behavior recognition based on a 3D deep convolutional network. Background technique [0002] In the field of computer vision, the research on action recognition has gone through more than 10 years. As an important part of pattern recognition, feature engineering has always been dominant in the field of behavior recognition. Before deep learning, scientists Evan Laptev and Cordelia Schmid of the French computer vision institution Inria made the most outstanding contributions to the learning of behavioral features. Similar to the ILSVRC Image Recognition Challenge, the action recognition-based challenge THUMOS continues to refresh recognition records every year. The behavioral feature calculation method introduced by Inria has always been among the best. Especially in 2013, Dr. WangHeng of Inria proposed a trajectory-based behavior feature calc...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the existing 3-dimensional convolutional network exists: the network can only extract the sub-motion state; every small segment of the video belongs to the same behavior category; the existing behavior recognition network Only the sub-motion state can be extracted; every small segment of the video belongs to the same behavior category; the scale and duration of each input video segment must be fixed. processing; at the same time, the network learns short-term motion features, lacking complete behavioral information

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