SYSTEM AND METHOD FOR HUMAN DETECTION AND COUNTING USING BACKGROUND MODELING, HOG AND HAAR FEATURES

摘要

A system for adaptive learning based human detection for channel input of captured human image signals, the system comprising: a sensor for tracking real-time images of an environment of interest; a feature extraction and classifiers generation processor for extracting a plurality of features and classifying the features associated with time-space descriptors of image comprising background modeling, Histogram of Oriented Gradients (HOG) and Haar like wavelet; a processor configured to process extracted feature classifiers associated with plurality of real-time images; combine the plurality of feature classifiers of time-space descriptors; evaluate a linear probability of human detection based on a predetermined threshold value of the feature classifiers in a time window having at least one image frame; a counter for counting the number of humans in the real-time images; and a transmission device configured to send the final human detection decision and number thereof to a storage device.