Therefore, it is a promising research direction to combine the advantages of different sensing sources, because each modality has complementary benefits and drawbacks, as has been shown in other works Bellotto and Hu [5], St-Laurent et al. [6], M. Hofmann and Rigoll [7], Johnson and Bajcsy [8], Zin et al. [9].Additional requirements for our application arise from the fact that the low-cost thermal sensor provides a low resolution image and, therefore, does not allow us to build accurate models for detecting people. Moreover, in order to have a high reaction capability, we are looking for solutions that allow parallel processing of all the input data instead of sequentially.

Therefore, the chosen approach is:To combine machine learning paradigms with computer vision techniques in order to perform image classification: first, we apply transformations using computer vision techniques, and second, we perform classification using machine learning paradigms.To construct a hierarchical classifier combining the three sensor source data (images) to improve person detection accuracy.We have evaluated the system in two different real scenarios: a manufacturing shop floor, where machines and humans share the space while performing production activities, and a science museum with different elements exposed, people moving around and strong illumination changes, due to weather conditions. Experimental results seem promising considering that the percentage of wrong classifications using only Kinect-based detection algorithms is drastically reduced.

The rest of the paper is organized as follows: In Section II, related work in the area of human detection is presented. We concentrate mainly on work done using machine learning for people detection. Section III describes the proposed approach and Section IV, the experimental evaluation. Section V shows experimental results and Section VI, conclusions and future work.2.?Related WorkPeople detection and tracking systems have been studied extensively because of the increasing demand for advanced robots that must integrate natural human-robot interaction (HRI) capabilities to perform some specific tasks for the humans or in collaboration with them. A complete review on people detection is beyond the scope of this work; extensive work can be found in Schiele [10] and Cielniak Drug_discovery [11]. We focus on the recent related work.To our knowledge, two approaches are commonly used for detecting people on a mobile robot: (1) vision-based techniques; and (2) combining vision with other modalities, normally range sensors, such as laser scanners or sonars, like in Wilhelm et al. [12], Scheutz et al. [13], Martin et al. [14]. Martin et al.