Abstract-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 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.
In REF , multidimensional features for laser scanner pedestrian detection are used, and Histograms of Oriented Gradients (HOG) features and Support Vector Matching (SVM) for visual detection is performed by a Bayesian model approach.
Human detection using multimodal and multidimensional features
2008 IEEE International Conference on Robotics and Automation
2008 IEEE International Conference on Robotics and Automation
Computer Science