A Confusion Matrix-based High-Level Semantic Video Behavior Recognition Method

Abstract

The invention discloses a high-level semantic behavior video recognition method based on confusion matrix, which mainly solves the problem of low recognition rate caused by confusion in the prior art. The implementation steps are: 1) extract the dense trajectory of the behavior video, and obtain the underlying features of the trajectory; 2) use the underlying features to perform behavior recognition and obtain the confusion matrix; 3) for the confusion matrix, define a high-level semantic list that can distinguish confusing behaviors; 4 ) associating the high-level semantic list with the behavioral video data, training a corresponding discriminative classifier for each high-level semantics, and concatenating the judgment scores of the behavioral video under all discriminative classifiers to obtain a high-level semantic feature vector; 5) Obtain the membership degree of the underlying feature under the linear SVM classifier, combine the membership degree and the high-level semantic feature vector, and train the LSVM classifier for behavior recognition. The invention can correct the confusing behavior in a targeted manner, improves the accuracy of behavior recognition, and can be used for video monitoring.

Description

technical field [0001] The invention belongs to the technical field of video image processing, and in particular relates to a video behavior recognition method, which can be used for video monitoring. Background technique [0002] In recent years, the increasingly important academic value, economic benefit and social value of behavior recognition and video classification have attracted the attention of many scholars and become a challenging research topic in the field of computer vision. New human-computer interaction, virtual reality, video coding and transmission, game control and many other aspects have broad application prospects. With the deepening of research work in the field of video behavior recognition, researchers have found that low-level features have very limited ability to describe complex video behaviors, and research hotspots have gradually shifted from the design and extraction of new low-level features to the definition of high-level semantics and the cons...

AI Technical Summary

Problems solved by technology

However, the learning of objects and poses requires additional learning of object detectors and pose detectors, and object detectors need to be implemented on the basis of accurate object detection, which is difficult to apply to complex human behaviors.

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