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Fig. 1: We present a novel place recognition system that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image. The proposed system utilizes convolutional network features as robust landmark descriptors to recognize places despite severe viewpoint and condition changes, without requiring any environment-specific(More)
— After the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic(More)
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 handling environmental changes. In this paper, a new method for object detection is introduced that combines(More)
— 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 to be employed by autonomous robots in real world environments, they must be adapted to perform(More)
— To have a robot actively supporting a human during a collaborative task, it is crucial that robots are able to identify the current action in order to predict the next one. Common approaches make use of high-level knowledge, such as object affordances, semantics or understanding of actions in terms of pre-and post-conditions. These approaches often(More)
We propose a new task of unsupervised action detection by action matching. Given two long videos, the objective is to temporally detect all pairs of matching video segments. A pair of video segments are matched if they share the same human action. The task is category independent—it does not matter what action is being performed—and no supervision is used(More)
In this paper, a robust stereo matching technique using growing component seeds has been proposed in order to find a semi-dense disparity map. In the proposed algorithm only a fraction of the disparity space is checked to find a semi-dense disparity map. The method is a combination of both the feature based and the area based matching algorithms. The(More)
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