Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preve...
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Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.
Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices....
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Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices. In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier. Specifically, we construct a Visual Confusion Label Tree based on the output of the convolution neural network models, and use a multi-kernel SVM plus classifier with hierarchical constraints to train the tree classifier. Focusing on those confusion subsets instead of the entire set of categories makes the tree classifier more discriminative and the replacement of the fully-connected layers reduces the original running time. Experiments show that our tree classifier obtains a significant improvement over the state-of-the-art tree classifier by 4.3% and 2.4% in terms of top-1 accuracy on CIFAR-100 and ImageNet datasets respectively. Additionally, our method achieves 124× and 115× speedup ratio compared with fully-connected layers on AlexNet and VGG16 without accuracy decline.
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consis...
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In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from bei...
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In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from being well solved. The challenges stem from not only the computation complexity in large-scale map merging but also the inefficiency to enable the parallel computing in this process, which is indispensable for us to make avail of the frontier of computing technology such as powerful cloud infrastructure. To effectively address these challenges, especially the latter one, we propose a scalable and real-time multi-robot visual SLAM framework based on the cloud robotic paradigm. The prominent feature of our framework is that it can distribute the SLAM process to multiple computing hosts in a cluster, which enables map building in parallel. To eliminate the bottleneck from data sharing between different sub-tasks, we also introduce diversified messaging pattern for various messaging scenarios, as well as the consistency policies for map data. The evaluations on the prototype of our framework, have shown that our method can do support as many as 256 robot entities simultaneously, without any compromising on the precision of poses estimation and map building.
With the popularity of the open source, the open source community has accumulated a large number of open source project. While these massive projects provide developers with rich reusable resource, they also bring dif...
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ISBN:
(纸本)9781538665664;9781538665657
With the popularity of the open source, the open source community has accumulated a large number of open source project. While these massive projects provide developers with rich reusable resource, they also bring difficulties for users to choose the appropriate software. Therefore, it is very meaningful to rank the software and tell users which is better. In this paper, we propose a novel approach that ranks software based on a global perspective different from traditional software evaluation and ranking methods. We evaluate the software from four dimensions, namely community popularity, development activity, software health and team health. Each dimension contains some metrics. We demonstrate the effectiveness of our method through comparative experiments. This method has been integrated into to the OSSEAN platform to form a software leaderboard.
Area coverage path planning is a special path planning method, which requires the robot to go through every point except obstacles in workspace. It has been used in many fields, such as lawn mowing, snow removal, sear...
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ISBN:
(纸本)9781538674178
Area coverage path planning is a special path planning method, which requires the robot to go through every point except obstacles in workspace. It has been used in many fields, such as lawn mowing, snow removal, search and rescue task, pesticide spraying, demining robots, cleaning robots and so on. This paper presents the achievements of the last two decades on area coverage. We propose a new Classification based on method of Choset. We analyze and summarize the recently research results by the new classification method and list the advantages and disadvantages. Finally, we give a summary of the classification methods and propose future research directions.
Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential rel...
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Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more *** kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.
Similarity of nominal data plays fundamental roles in numerous fields of both machine learning and data mining. Unlike the similarity of numerical data, that of nominal data is much more difficult to describe, and few...
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Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of back-grounds and become hard examples during training. Compared with those proposal-based ones, real-time detecto...
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