Dynamic and static characteristic-based video classification method

Abstract

The invention discloses a dynamic and static characteristic-based video classification method. The problem that the video classification accuracy is not high is solved. The method comprises the stepsthat dynamic characteristics and static characteristics in video are processed, information is fused by means of Cholesky conversion, and then video classification is completed by using a GRU neural network; the dynamic characteristics of each video frame are captured through a DT algorithm, all video frames are isolated through a DBSCAN clustering algorithm, a motion frame is built in each frameof each video clip, the motion frames between the adjacent frames of each video clip are connected, and capturing and tracking of the dynamic characteristics are completed; by means of HoG and BoW methods, a dynamic information histogram generated by the dynamic characteristics and a static information histogram generated by a CNN neural network are fused by means of Cholesky conversion; finally,the GRU neural network is utilized for achieving video classification. Accordingly, by processing the dynamic and static information separately, the accuracy of video classification can be improved, and good implementation and robustness are achieved.

Description

technical field [0001] The invention relates to a video classification method based on dynamic and static features, and belongs to the cross technical fields of behavior recognition, machine learning and the like. Background technique [0002] In recent years, action recognition and classification in videos has become an important research topic in the field of computer vision, which has important theoretical significance and practical application value. [0003] With the development of my country's economy and society and the advancement of science and technology, the identification, analysis and understanding of tasks in videos has become an important content in the fields of social science and natural science. Has a wide range of applications. Compared with behavior recognition in static pictures, background changes in videos, tracking of dynamic objects, and high-dimensional data processing are more complex and therefore more challenging. [0004] The recognition of hum...

AI Technical Summary

Problems solved by technology

The advantage of the optical flow method is that it can detect moving objects without knowing any information about the scene, but the computational complexity is high, the real-time performance is poor, and it has high requirements for hardware.

While training a standard RNN to solve problems that need to learn long-term temporal dependencies is not ideal

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