Method for motion recognition for simulating human visual cortex perception mechanism

Abstract

The invention discloses a method for motion recognition for simulating a human visual cortex perception mechanism. The method comprises the steps that preprocessing is conducted on a video image sequence; feature extraction is conducted, an S1 unit model is established, and tensor local maximum filtering processing is conducted through the S1 unit model so that a C1 unit model can be obtained; a fragment is randomly extracted from the C1 unit model obtained from a training stage, template matching is conducted on the fragment and the C1 unit model so that an S2 unit model can be obtained, global maximum filtering is conducted on the S2 unit model, and therefore a C2 unit model is obtained; a fragment is randomly extracted from the C2 unit model obtained from the training stage and template matching is conducted on the fragment and the C2 unit model so that an S3 unit model can be obtained, global maximum filtering is conducted on the S3 unit model, and therefore a feature tensor C3 unit is obtained; the feature extraction processing is successively conducted on a preprocessed training sample and a preprocessed testing sample, and therefore the feature tensor C3 is obtained; the feature tensor C3 is input into a classifier for classification. The method can effectively, rapidly and accurately identify the action of a movement target.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to an action recognition method for simulating the perception mechanism of human visual cortex. Background technique [0002] In the field of computer vision, action recognition is a research hotspot that has received extensive attention in recent years. In the field of video surveillance, traditional video surveillance methods are unable to get rid of human monitoring and management. Since people's attention to video surveillance gradually decays with time, long-term traditional video surveillance is often inefficient and has a high loss of alarm rate. Traditional video surveillance often only plays the role of forensics after a crime has occurred. If the video sequence can be automatically analyzed and processed to identify the movements of the people in it, then video surveillance can also be used to prevent crimes and give early warning of sudden dangers, thus playing a...

AI Technical Summary

Problems solved by technology

However, the robustness of these action recognition methods is not strong enough, and they are very sensitive to changes in the size of moving targets and changes in movement speed within a certain range.

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