@article {68,
	title = {Headgear Accessories Classification Using an Overhead Depth Sensor},
	journal = {Sensors},
	volume = {17},
	year = {2017},
	month = {08/2017},
	pages = {1845},
	chapter = {1845},
	doi = {10.3390/s17081845},
	url = {http://www.mdpi.com/1424-8220/17/8/1845http://www.mdpi.com/1424-8220/17/8/1845/pdf}
}
@conference {62,
	title = {Robust People Detection and Tracking from an overhead Time-of-Flight Camera},
	booktitle = {12th International Conference on Computer Vision Theory and Applications.},
	year = {2017},
	month = {03/2017},
	pages = {556-564},
	address = {Porto, Portugal},
	abstract = {In this paper we describe a system for robust detection of people in a scene, by using an overhead Time of Flight (ToF) camera. The proposal addresses the problem of robust detection of people, by three means: a carefully designed algorithm to select regions of interest as candidates to belong to people; the generation of a robust feature vector that efficiently model the human upper body; and a people classification stage, to allow robust discrimination of people and other objects in the scene. The proposal also includes a particle filter tracker to allow people identification and tracking. Two classifiers are evaluated, based on Principal Component Analysis (PCA), and Support Vector Machines (SVM). The evaluation is carried out on a subset of a carefully designed dataset with a broad variety of conditions, providing results comparing the PCA and SVM approaches, and also the performance impact of the tracker, with satisfactory results.},
	isbn = {978-989-758-225-7},
	doi = {10.5220/0006169905560564},
	author = {Fernandez-Rincon, Alvaro and Fuentes-Jimenez, David and Cristina Losada-Gutierrez and Marta Marron-Romera and Luna, Carlos A. and Javier Macias-Guarasa and Manuel Mazo}
}
@conference {63,
	title = {Robust system for partially occluded people detection in RGB images},
	booktitle = {12th International Conference on Computer Vision Theory and Applications.},
	year = {2017},
	month = {03/2017},
	pages = {532-539},
	abstract = {This work presents a robust system for people detection in RGB images. The proposal increases the robustness of previous approaches against partial occlusions, and it is based on a bank of individual detectors whose results are combined using a multimodal association algorithm. Each individual detector is trained for a different body part (full body, half top, half bottom, half left and half right body parts). It consists of two elements: a feature extractor that obtains a Histogram of Oriented Gradients (HOG) descriptor, and a Support Vector Machine (SVM) for classification. Several experimental tests have been carried out in order to validate the proposal, using INRIA and CAVIAR datasets, that have been widely used by the scientific community. The obtained results show that the association of all the body part detections presents a better accuracy that any of the parts individually. Regarding the body parts, the best results have been obtained for the full body and half top body.
},
	isbn = {978-989-758-225-7)},
	doi = {10.5220/0006165005320539},
	author = {Marcos Baptista-Rios and Marta Marron-Romera and Cristina Losada-Gutierrez and Cruz Lozano, Jose Angel and Abril, Antonio del}
}
