Behavior recognition method based on spatiotemporal volume of local joint point trajectory in skeleton sequence

Abstract

The invention belongs to the technical field of artificial intelligence, and discloses a behavior recognition method based on local joint point trajectory space-time volume in a skeleton sequence, which extracts the local joint point trajectory space-time volume from input RGB video data and skeleton joint point data; The pre-training model of the video data set extracts image features; constructs a codebook for each different feature of each joint point in the training set and encodes it separately, and concatenates the features of n joint points into a feature vector; uses the SVM classifier Behavior classification and identification. The present invention fuses manual features and deep learning features, and uses deep learning methods to extract local features, and the fusion of multiple features can achieve a stable and accurate recognition rate; the present invention uses the 2D human skeleton estimated by the pose estimation algorithm and the RGB video sequence. Extracting features has low cost and high accuracy, and it is of great significance to apply to real scenes.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a behavior recognition method based on the spatio-temporal volume of local joint point trajectories in a skeleton sequence. Specifically, it is a behavior recognition method based on the spatiotemporal volume of local joint point trajectories in RGB and 2D skeleton sequences. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] With the development of artificial intelligence technology and the increasing investment from the government and industry, the artificial intelligence industry is booming and has become a hot spot of scientific research today. The popularity of artificial intelligence applications has an increasingly prominent impact on society, and has a positive impact on people's livelihoods such as smart transportation, smart home, and smart medical care. As the core for...

AI Technical Summary

Problems solved by technology

[0010] (1) The skeleton-based behavior recognition method using depth information is difficult to apply to real scenes due to the high cost of depth detectors and low accuracy when applied to real outdoor scenes with complex scenes

[0011] (2) It is difficult to model the time information by the skeleton recognition method using the global joint point trajectory calculation feature

[0012] (3) The iDT method requires dense sampling and tracking of interest points in the human body area, and a large number of samples make the trajectory redundant

[0016] (2) Since the dimension of the feature depends on the length of the video and the length of the video is different, this leads to the difference in the length of the trajectory of the joint points of each video and its feature dimension

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