Abstract: Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform. The set of sensors is set up combining the rich data source offered by a Kinect sensor, which provides vision and depth at low cost, and a thermopile array sensor. Experimental results carried out with a mobile platform in a manufacturing shop floor and in a science museum have shown that the false positive rate achieved using any single cue is drastically reduced. The performance of our algorithm improves other well-known approaches, such as C4 and histogram of oriented gradients (HOG).
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Susperregi, L.; Sierra, B.; Castrillón, M.; Lorenzo, J.; Martínez-Otzeta, J.M.; Lazkano, E. On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot. Sensors 2013, 13, 14687-14713.
Susperregi L, Sierra B, Castrillón M, Lorenzo J, Martínez-Otzeta JM, Lazkano E. On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot. Sensors. 2013; 13(11):14687-14713.
Susperregi, Loreto; Sierra, Basilio; Castrillón, Modesto; Lorenzo, Javier; Martínez-Otzeta, Jose M.; Lazkano, Elena. 2013. "On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot." Sensors 13, no. 11: 14687-14713.