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For resolving the problem of rotation-invariant human detection in natural scene, a rotation-invariant detection algorithm based on polar-HOGs and double-scale direction estimation is proposed in this paper. The algorithm first transforms rotation of object to cycle translation by using polar coordinate mapping, then eliminates the effect of rotation by applying reverse cycle translation to p-θ mapping...
This paper describes a novel technique for detecting a human body direction using SVM constructed by HOG feature selected by AdaBoost. HOG feature is well-known feature for the robust judgment of a human. We employ the feature for detecting a human body direction. We compared some feature selecting methods with the previous one. Experimental results show effectiveness of the proposed method.
In this paper we discuss the issue of classifiers combined with histogram of oriented gradients (HOG) descriptors for human detection. And we present a method that combines AdaBoost learning with HOG descriptors. The weak learners used in our algorithm are based on weighted modified quadratic discriminant functions (MQDF) which is a parametric model. We evaluate our algorithm on the INRIA person dataset...
We propose a method that can detect humans in a single image based on a novel cascaded structure. In our approach, both intensity-based rectangle features and gradient-based 1-D features are employed in the feature pool for weak-learner selection. The Real AdaBoost algorithm is used to select critical features from a combined feature set and learn the classifiers from the training images for each...
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