Center for Intelligent Systems Research

Burwood, Australia

Center for Intelligent Systems Research

Burwood, Australia

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Zhou H.,Center for Intelligent Systems Research | Kong H.,Massachusetts Institute of Technology | Alvarez J.M.,National ICT Australia | Creighton D.,Center for Intelligent Systems Research | Nahavandi S.,Center for Intelligent Systems Research
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2014

We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average. © 2014 IEEE.


Zhou H.,Center for Intelligent Systems Research | Kong H.,Massachusetts Institute of Technology | Wei L.,Center for Intelligent Systems Research | Creighton D.,Center for Intelligent Systems Research | Nahavandi S.,Center for Intelligent Systems Research
IEEE Transactions on Intelligent Transportation Systems | Year: 2015

An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground-vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 × 595 videos on average. © 2014 IEEE.


Goli M.,George Washington University | Eskandarian A.,Virginia Polytechnic Institute and State University | Eskandarian A.,George Washington University | Eskandarian A.,Center for Intelligent Systems Research
ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) | Year: 2015

This paper presents the problem of mobile robots specialized coordinated motion, namely platooning from an experimental point of view. An experimental set-up consisting of multiple mobile robots is developed to emulate a scaled version of real life connected vehicles which will move in a platoon formation for enhanced efficiency, safety, and energy conservations. The autonomous motion of multiple robots could be coordinated through wireless communications between them and a lead robot. Different aspects and requirements of an experimental platform to accomplish this mission are discussed. A platooning scenario using our connected mobile robots is demonstrated in this paper. The efficiency of the described platform in implementing vehicle platooning strategies and the behavior of propagation error when there is no communication between robots is observed from this experiment. Copyright © 2015 by ASME.


Zhou H.,Center for Intelligent Systems Research | Wei L.,Center for Intelligent Systems Research | Creighton D.,Center for Intelligent Systems Research | Nahavandi S.,Center for Intelligent Systems Research
Machine Vision and Applications | Year: 2014

Inpainting images with smooth curvilinear structures interrupted is a challenging problem, because the structures are salient features sensitive to the human vision system and they are not easy to be completed in a visually pleasing way, especially when gaps are large. In this paper, we propose an approach to address this problem. A curve with a desired nice shape is first created to smoothly extend the missing structure from the known to unknown regions. As the curve partitions the unknown region into separate areas, textures can be filled independently into each area. We then adopt a patch-based texture inpainting method enhanced by a novel similarity measurement of patches. After that, very abrupt edges caused by different inpainted colours on their two sides need to be smoothed for natural colour transition across the curve. Experimental results demonstrate the effectiveness of the proposed approach. © 2014, Springer-Verlag Berlin Heidelberg.

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