Track-energy-diffusion-diagram-based group behavior identification method

Abstract

The invention discloses a track-energy-diffusion-diagram-based group behavior identification method. The track-energy-diffusion-diagram-based group behavior identification method comprises the following steps: 1, carrying out multi-target detection and multi-target tracking on an original video and extracting a multi-target track; 2, converting the multi-target track into a track energy block diagram; 3, generating a track energy diffusion diagram by the track energy block diagram by virtue of an energy diffusion process; 4, generating a track energy equipotential line diagram by the track energy diffusion diagram; 5, carrying out feature extraction of the track energy equipotential line diagram, thereby obtaining fusion feature vectors by a canonical-correlation-analysis-based feature fusion algorithm; and 6, classifying and identifying the fusion feature vectors. According to the track-energy-diffusion-diagram-based group behavior identification method, the track energy block diagram has a high performance and is capable of saving the time information of the group behavior track; the track energy diffusion diagram is capable of smoothening noise randomly generated by movement; the multi-feature fusion is implemented by the canonical-correlation-analysis-based feature fusion algorithm, so that the identification accuracy is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps identify patterns or movements within an image through their temporal changes over time. It involves comparing different images taken at specific times together to find similarities across them. By doing this, we are able to make accurate predictions about future movement without relying solely upon past data. Overall, these techniques help us analyze complex datasets efficiently while maintaining important aspects such as recognizing moving objects from still frames captured during video recording.

Problems solved by technology

This patented describes different methods called groups action recognition, where each individual moves their own way around without being detected. These techniques involve analyzing multiple frames of images captured over consecutive periods of time together to identify specific actions within them. However, current systems have difficulty accurately identifying complicated group movements involving many objects simultaneously because there may exist various factors affecting how similar individuals behave differently at any given moment.

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