5 research outputs found
Real-time 3D human tracking for mobile robots with multisensors
© 2017 IEEE. Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system consists of three sub-modules: monocular camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor fusion. An improved visual tracking algorithm is presented to provide partial location estimation (2-D). The algorithm is designed to overcome severe occlusions, scale variation, target missing and achieve robust re-detection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D). EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the superior performance of the 3-D tracking system in terms of both the accuracy and robustness
Improving Model Drift for Robust Object Tracking
Discriminative correlation filters show excellent performance in object
tracking. However, in complex scenes, the apparent characteristics of the
tracked target are variable, which makes it easy to pollute the model and cause
the model drift. In this paper, considering that the secondary peak has a
greater impact on the model update, we propose a method for detecting the
primary and secondary peaks of the response map. Secondly, a novel confidence
function which uses the adaptive update discriminant mechanism is proposed,
which yield good robustness. Thirdly, we propose a robust tracker with
correlation filters, which uses hand-crafted features and can improve model
drift in complex scenes. Finally, in order to cope with the current trackers'
multi-feature response merge, we propose a simple exponential adaptive merge
approach. Extensive experiments are performed on OTB2013, OTB100 and TC128
datasets. Our approach performs superiorly against several state-of-the-art
trackers while runs at speed in real time.Comment: 7 pages, 6 figures, 4 table
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
The vital statistics of the last century highlight a sharp increment of the
average age of the world population with a consequent growth of the number of
older people. Service robotics applications have the potentiality to provide
systems and tools to support the autonomous and self-sufficient older adults in
their houses in everyday life, thereby avoiding the task of monitoring them
with third parties. In this context, we propose a cost-effective modular
solution to detect and follow a person in an indoor, domestic environment. We
exploited the latest advancements in deep learning optimization techniques, and
we compared different neural network accelerators to provide a robust and
flexible person-following system at the edge. Our proposed cost-effective and
power-efficient solution is fully-integrable with pre-existing navigation
stacks and creates the foundations for the development of fully-autonomous and
self-contained service robotics applications