A method and system for behavior recognition based on middle-level features

Abstract

The invention discloses a behavior recognition method and system based on middle-level features, wherein the implementation of the method includes: obtaining a candidate component detector set from a sample image sequence; removing B% component detection with weak discriminative ability from the candidate component detector set to obtain a new set of candidate component detectors; according to the weight of each component detector in the new candidate component detector set, sort from large to small, and select the top P component detectors as the class A behavior category Middle-level feature extractor: obtain the middle-level feature extractor of each type of behavior category in the behavior category, combine it into a bag of words, use the bag of words to extract the sample middle-level features of the sample image sequence, use the sample middle-level features to train the classifier, and obtain a behavior recognition classifier ; Input the test image sequence into the behavior recognition classifier to obtain the behavior category of the test image sequence. The invention has strong recognition ability, high recognition accuracy, strong practicability, and retains the correlation between components.

Description

technical field [0001] The invention belongs to the field of computer vision, and more specifically relates to a behavior recognition method and system based on middle-level features. Background technique [0002] Behavior recognition technology is the core technology in the application fields of video security monitoring, human-computer interaction, video retrieval and analysis, etc., and it has attracted more and more attention from industry and academia. However, behavior analysis in videos poses great challenges due to the great disturbances in behaviors, such as motion blur, scale change, low resolution, background noise, camera motion, and viewpoint changes. [0003] Existing methods mainly include the following two main lines: The first is low-level spatiotemporal local features, such as: spatiotemporal interest points, gradient-based features, and trajectory features. Typically, a large number of local descriptors are extracted from the video training set, then a "b...

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Problems solved by technology

[0006] Aiming at the above defects or improvement needs of the prior art, the present invention provides a behavior recognition method and system based on middle-level features, thereby solving the problem that the existing behavior recognition has weak recognition ability, requires a lot of manpower and material resources, is not practical, and is lost. Technical issues of associativity between components

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