Behavior Recognition Method Based on Joint Statistical Descriptor in Wavelet Domain

A recognition method and wavelet domain technology, applied in the field of video processing, can solve problems such as low feature dimension, unconsidered coefficient direction, relationship between coefficients, insufficient data coverage, etc., to achieve effective extraction and reduce impact

Active Publication Date: 2020-04-07
XIDIAN UNIV
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Problems solved by technology

This method first extracts spatio-temporal interest points, and then performs two-dimensional wavelet decomposition in the cube around the interest points to obtain discriminative and reliable descriptors. The feature dimension is low, and it has certain tolerance to the influence of noise and illumination. However, this method only selects three representative planes in the local cube when performing wavelet decomposition, which does not cover enough data

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  • Behavior Recognition Method Based on Joint Statistical Descriptor in Wavelet Domain
  • Behavior Recognition Method Based on Joint Statistical Descriptor in Wavelet Domain
  • Behavior Recognition Method Based on Joint Statistical Descriptor in Wavelet Domain

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[0027] Reference figure 1 , The method of behavior recognition based on wavelet domain joint statistical descriptors of the present invention, the steps are as follows:

[0028] Step 1: Densely sample the behavior video to extract the dense trajectory of the video sequence.

[0029] Common trajectory extraction methods include trajectory tracking based on KLT (Kanade-Lucas-Tomasi), trajectory tracking based on SIFT (Scale Invariant Feature Transform) descriptor matching, and trajectory tracking based on dense optical flow. The present invention uses the dense optical flow-based trajectory tracking method proposed by Wang et al. in the article "Action recognition by dense trajectories" in 2011 to extract the motion trajectory of the behavior video, and the steps are as follows:

[0030] (1.1) Use dense grids to densely sample the video in eight scale spaces in turn, and the scaling factor between every two scale spaces is The sampling interval is 5 pixels;

[0031] (1.2) Calculate th...

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Abstract

The invention discloses a behavior recognition method based on joint statistical descriptors in wavelet domain, which mainly solves the problems of insufficient time-space information extraction, insufficient consideration of relationship between coefficients and low robustness in existing wavelet domain behavior recognition technology. The technical solution is: 1. Extract the dense trajectory of the behavioral video, and construct a cube curved along the trajectory; 2. Decompose the video with 3D stationary wavelet transform to obtain coefficient subbands in each direction; 3. In the cube curved along the trajectory , to construct the mutual information descriptor between the wavelet coefficient subbands; 4. In the cube curved along the trajectory, construct the co-occurrence histogram descriptor of wavelet coefficients at all levels according to different neighborhood directions; 5. Concatenate the mutual information descriptor and the co-occurrence histogram The histogram descriptor is obtained, and the joint statistical descriptor in the wavelet domain is obtained; 6. The bag-of-words model of the descriptor is constructed, and the SVM classifier is used for identification. The invention improves the accuracy rate of human behavior recognition and can be applied to intelligent monitoring and human-computer interaction.

Description

technical field [0001] The invention belongs to the technical field of video processing, and further relates to a behavior recognition method, which can be used for intelligent video monitoring and human-computer interaction. Background technique [0002] In the previous decades, human behavior recognition and its related research fields have always been considered to be very challenging scientific research directions in the computer vision discipline. Human behavior recognition allows computers to learn, analyze, understand and memorize human behavior through algorithm design, and realize computer classification and discrimination of human behavior videos. Technologies related to human behavior recognition are widely used, such as camera monitoring, multimedia semantic annotation and indexing, pedestrian tracking and human-computer interaction, etc. More and more scholars and institutions have carried out a lot of research work in related fields. The essence of behavior cl...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/42G06F18/2411
Inventor 同鸣李金鹏
Owner XIDIAN UNIV
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