Pedestrian detection method and apparatus based on Haar-like intermediate layer filtering features

Abstract

The present invention discloses a pedestrian detection method based on Haar-like intermediate layer filtering features, comprising the steps of: extracting object features of various training images in a training image set, training an Adaboost classifier based on decision trees by using the extracted object feature data to obtain a classification model; extracting object features of an image to be detected under a plurality of scales and inputting the object features to the classification model to obtain a pedestrian detection result, wherein a method for extracting the object features comprises the following steps: respectively extracting a plurality of different channel features of an original image to obtain multiple channel feature patterns of the original image; respectively performing downsampling for each channel feature pattern; respectively extracting corresponding Haar-like features of each channel feature pattern which has been subjected to the downsampling by using a group of preset Haar-like feature templates; and clustering all the Haar-like features of the original image into the object features of the original image. The invention also discloses a pedestrian detection apparatus based on Haar-like intermediate layer filtering features. The pedestrian detection method and the pedestrian detection apparatus can effectively improve pedestrian detection performance.

Description

technical field [0001] The invention relates to the intersecting technical field of pattern recognition, image processing and computer vision, in particular to a pedestrian detection method and device based on a Harr-like (Haar-like) intermediate layer filtering feature. Background technique [0002] With the development of computer vision technology, pedestrian detection technology has become a research hotspot at this stage and even for a long time in the future. Pedestrian detection can be divided into pedestrian detection in motion (or called target tracking), and pedestrian detection in static pictures, that is, the pedestrian detection in static pictures studied by the present invention. Pedestrian detection in static pictures is a process of using a computer to determine whether a given single image or a frame in a video contains pedestrians, and if pedestrians are included, it is necessary to further locate the pedestrian's position. Pedestrian detection is the prem...

AI Technical Summary

Problems solved by technology

[0004] Although the above-mentioned pedestrian detection method based on ACF features can describe pedestrian information as a whole, it has also achieved good detection results, but due to the lack of description of the local structural features of the human body, the algorithm cannot filter some non-pedestrian detection windows. There is room for further improvement

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