Robust people detection using depth information from an overhead Time-of-Flight camera. (1st April 2017)

• Record Type: Journal Article
• Authors: Luna, Carlos A.; Losada-Gutierrez, Cristina; Fuentes-Jimenez, David; Fernandez-Rincon, Alvaro; Mazo, Manuel; Macias-Guarasa, Javier
• Is Part Of: Expert systems with applications. Volume 71(2017)
• Page Start: 240
• Page End: 256
• Publication Date: 2017-04-01
• Subjects: People detection -- Depth camera information -- Interest regions estimation -- Overhead camera -- Feature extraction; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.11.019
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Robust system to detect people only using depth information from a ToF camera. Refined algorithm for determining the regions of interest. Classifier stage to distinguish between people and other objects in the scene. Rigorous experimental procedure shows high performance with a broad user variability. Generated database will be made available to the research community. Abstract: In this paper we describe a system for the automatic detection of multiple people in a scene, by only using depth information provided by a Time of Flight (ToF) camera placed in overhead position. The main contribution of this work lies in the proposal of a methodology for determining the Regions of Interest (ROI's) and feature extraction, which result in a robust discrimination between people with or without accessories and objects (either static or dynamic), even when people and objects are close together. Since only depth information is used, the developed system guarantees users' privacy. The designed algorithm includes two stages: an online stage, and an offline one. In the offline stage, a new depth image dataset has been recorded and labeled, and the labeled images have been used to train a classifier. The online stage is based on robustly detecting local maximums in the depth image (which are candidates to correspond to the head of the people present in the scene), from which a carefully ROI is defined around each of them. For each ROI, a feature vector is extracted, providing information on the top view of people and objects, including information related to the expected overhead morphology of the head and shoulders. The online stage also includes a pre-filtering process, in order to reduce noise in the depth images. Finally, there is a classification process based on Principal Components Analysis (PCA). The online stage works in real time at an average of 150 fps. In order to evaluate the proposal, a wide experimental validation has been carried out, including different number of people simultaneously present in the scene, as well as people with different heights, complexions, and accessories. The obtained results are very satisfactory, with a 3.1% average error rate.