People tracking by cross modal association of vision sensors and acceleration sensors
ABSTRACT To realize accurate tracking of people in the environment, many studies have been proposed using vision sensors, floor sensors, and wearable devices. The problem of using vision sensors is that they do not provide ID information of each people and there are ambiguities when people come across. To solve the problem, we propose to combine acceleration sensors that are attached to the human body. Since the signals from vision sensors and acceleration sensors synchronize when they observe same person who are acting or walking in the environment, these signals are not independent. The correlation between the signals is evaluated based on the canonical correlation analysis. Experimental results are shown to detect gesture and to track people to confirm the effectiveness of the proposed method.
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ABSTRACT: In this paper, we address the problem of realizing a human following task in a crowded environment. We consider an active perception system, consisting of a camera mounted on a pan-tilt unit and a 360° RFID detection system, both embedded on a mobile robot. To perform such a task, it is necessary to efficiently track humans in crowds. In a first step, we have dealt with this problem using the particle filtering framework because it enables the fusion of heterogeneous data, which improves the tracking robustness. In a second step, we have considered the problem of controlling the robot motion to make the robot follow the person of interest. To this aim, we have designed a multi-sensor-based control strategy based on the tracker outputs and on the RFID data. Finally, we have implemented the tracker and the control strategy on our robot. The obtained experimental results highlight the relevance of the developed perceptual functions. Possible extensions of this work are discussed at the end of the article.Computer Vision and Image Understanding. 01/2010;