Multi-characteristic multi-model pedestrian detection method

Abstract

The invention discloses a multi-characteristic multi-model pedestrian detection method, comprising steps of using an ICF+Adaboost classifier A to process a video frame RGB image, using a foreground-mask-based pedestrian detection classifier to process the foreground mask, combining results of the two classifiers, dividing the results into a high confidence level pedestrian detection result and a low confidence level pedestrian detection result according to a threshold value, using an ICF+Adaboost classifier B and a DPM pedestrian detection classifier to perform respective detection on the low confidence level pedestrian detection result, combining detection results of the two classifiers, using a detection score, an overlapping ratio, a width-height ratio, a classifier sequence number and a foreground ratio of each detected pedestrian as characteristic vectors, inputting the characteristic vectors into a ruling SVM to determine whether the detected pedestrian is correct pedestrian detection, outputting a new pedestrian detection result and combining the new pedestrian detection result and the high confidence level pedestrian detection result into a set as a final detection result. The multi-characteristic multi-model pedestrian detection method effectively solves the problem in the prior art that the misjudgment rate is high, and improves the detection rate.

Description

technical field [0001] The invention relates to the technical field of video analysis, in particular to a multi-feature and multi-model pedestrian detection method. Background technique [0002] At present, there are many pedestrian detection methods, such as Integrated Channel Features (ICF: Integrated Channel Features) + Adaboost, HOG + SVM (HOG: Histograms of Oriented Gradients, SVM: Support Vector Machine), DPM (Deformable Part Model) model, and based on Deep learning detection and foreground mask-based detection methods, etc., when these methods are applied to actual surveillance video, there are often problems that are difficult to be compatible between high detection rate and low false positive rate at the same time, thus giving further advanced video analysis (such as pedestrian retrieval) brings difficulties. [0003] For a single pedestrian detection method based on machine learning, the main problem is that it cannot be well adapted to various actual scenes. It c...

AI Technical Summary

Problems solved by technology

Various detection methods have incompatible contradictions between high detection rate and low false alarm rate, that is, it is difficult to obtain a pedestrian detection effect with wide adaptability, high detection rate, low false positive rate, and relatively fast speed with one method

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