Global template matching for guiding the learning of human detector

This work investigates a semantic-driven human detection algorithm, which employs global human template matching to inspire the local features based Adaboosting algorithm. We use distance transform to analyze distances between training samples and human contour template to obtain a classifier based on human outline features. At the training stage, the global outline feature will be coordinated into the Adaboost framework to guide the learning of a set of HOGs local classifiers. In other words, we make a stronger human classifier by dynamically tuning the hyper-plane of the support vector machine to combine both global and local features for attaining a better accuracy. A global fused error rate is also proposed to enhance the modified Adaboost iterative calculation such that a human detector called strong classifier can be obtained. The experiments illustrate that the detection rate received from the presented framework is above 20% better than original HOGs-based human detector.