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Autor:
Sünderhauf, Niko, Brock, Oliver, Scheirer, Walter, Hadsell, Raia, Fox, Dieter, Leitner, Jürgen, Upcroft, Ben, Abbeel, Pieter, Burgard, Wolfram, Milford, Michael, Corke, Peter
The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learn
Externí odkaz:
http://arxiv.org/abs/1804.06557
Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1]. Current safety inspections are labour intensive and limited by human fallibility,making au
Externí odkaz:
http://arxiv.org/abs/1706.06718
Autor:
Sa, Inkyu, Lehnert, Chris, English, Andrew, McCool, Chris, Dayoub, Feras, Upcroft, Ben, Perez, Tristan
This paper presents a 3D visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting the peduncle cleanly is one of the most difficult stages of the harvesting process, where the pe
Externí odkaz:
http://arxiv.org/abs/1701.08608
Autor:
Chen, Zetao, Jacobson, Adam, Sunderhauf, Niko, Upcroft, Ben, Liu, Lingqiao, Shen, Chunhua, Reid, Ian, Milford, Michael
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recog
Externí odkaz:
http://arxiv.org/abs/1701.05105
Publikováno v:
Robotics and Automation (ICRA), 2017 IEEE International Conference on
Deep learning models have achieved state-of-the- art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera. If these models are
Externí odkaz:
http://arxiv.org/abs/1701.04925
Autor:
Leitner, Jürgen, Tow, Adam W., Dean, Jake E., Suenderhauf, Niko, Durham, Joseph W., Cooper, Matthew, Eich, Markus, Lehnert, Christopher, Mangels, Ruben, McCool, Christopher, Kujala, Peter, Nicholson, Lachlan, Pham, Trung, Sergeant, James, Wu, Liao, Zhang, Fangyi, Upcroft, Ben, Corke, Peter
Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all partici
Externí odkaz:
http://arxiv.org/abs/1609.05258
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking perf
Externí odkaz:
http://arxiv.org/abs/1602.00763
Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in ha
Externí odkaz:
http://arxiv.org/abs/1512.03424
Autor:
Ge, ZongYuan, Bewley, Alex, McCool, Christopher, Upcroft, Ben, Corke, Peter, Sanderson, Conrad
We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small in
Externí odkaz:
http://arxiv.org/abs/1511.09209