Human detection using multimodal and multidimensional features

Luciano Spinello, Roland Siegwart
2008 2008 IEEE International Conference on Robotics and Automation  
This paper presents a novel human detection method based on a Bayesian fusion approach using laser range data and camera images. Laser range data analysis groups data points with a novel graph cutting method. Therefore, it computes a belief to each cluster based on the evaluation of multidimensional features that describe geometrical properties. A person detection algorithm based on dense overlapping grid of Histograms of Oriented Gradients (HOG) is processed on the image area determined by
more » ... a determined by each laser cluster. The selection of HOG features and laser features is obtained through a learning process based on a cascade of linear Support Vector Machines (SVM). A technique to obtain conditional probabilities from a cascade of SVMs is here proposed in order to combine the two information together. The resulting human detection consists in a rich information that takes into account the distance of the cluster and the confidence level of both detection methods. We demonstrate the performance of this work on real-world data and different environments.
doi:10.1109/robot.2008.4543708 dblp:conf/icra/SpinelloS08 fatcat:o4yjhucwtrc2hodahfe6vz6l5i