engleski

Human Behavior Inspired Temporal Attention Control System for Multiple Object Tracking

An important ability for mobile robots is to process multiple tasks in complex environments. Since the sensor resources on a robot are limited, it is necessary to distribute the sensors attention to different tasks along the time scale. This paper proposes a human inspired temporal attention control method which aims at detecting multiple objects and estimating their poses with a single actuated camera. The proposed method is based on three criteria which are partially inspired by human behavior: (i) minimization of the overall object poses perception uncertainty and minimization of the variance of the perception uncertainty of different objects ; (ii) minimization the energy cost of the camera movements for completing the tasks ; (iii) maximization of the number of objects in the cameras field of view. Kalman filters are used to estimate the object poses and to determine the perception uncertainty. The proposed approach is tested in both simulation and an experiment on the robot. The results show that the proposed approach is able to switch the camera’s attention according to the objects states efficiently with a low frequency of the camera movements.

attention control; Kalman filter; multiple objects tracking

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