Second generation curvelet transform-based static human detection method

Abstract

The invention provides a second generation curvelet transform-based static human detection method, which mainly solves the problem of high detection false-alarm rate in the conventional human detection technology. The detection process comprises the following steps of: acquiring a negative sample through bootstrap operation of the negative sample, and forming a training sample set by the negativesample and other positive samples in a database; calculating curvelet transform-based feature vectors of all training samples to form a training sample feature set; performing classification trainingon the sample feature set by adopting an AdaBoost algorithm to obtain a classifier; inputting a to-be-tested image with any size, calculating curvelet transform-based feature vectors of all scan window images in the to-be-tested image; inputting the curvelet transform-based feature vectors of all scan window images into the obtained classifier for classification; and according to classification results, combining all scan windows classified as human by utilizing a main window merging method to form the final human detection result. The method has the advantages of high detection accuracy and low false-alarm rate, and can be used for classifying and detecting human in the image.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, relates to a human body detection method, and can be used for classifying and detecting human bodies and other complex objects in images. Background technique [0002] Human detection has many important applications in computer vision, such as video surveillance, smart cars and smart transportation, robotics and advanced human-computer interaction, etc. However, the appearance of the human body varies greatly due to factors such as changes in the human body's own posture, diversity of clothing, and illumination, making human body detection a very difficult problem. [0003] At present, the methods of human body detection in static images mainly include methods based on human body models, methods based on template matching and methods based on statistical classification. Human body model-based methods have explicit models, can handle occlusion, and can infer the pose of the human body...

AI Technical Summary

Problems solved by technology

[0008] Mohamed ElAroussi et al. have applied the method of curvelet transform to face detection, and proposed a face detection method based on block-based curvelet transform, using the statistical features extracted from the blocks divided by the coefficients of the curvelet transform as feature vectors. Face detection has achieved good detection results on ORL, YALE and FERET data sets, but no one has used curvelet transform for human body detection.

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