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568
Mining actionlet ensemble for action recognition with depth cameras
- In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), Providence, Rhode Island
, 2012
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RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
- The International Journal of Robotics Research
, 2012
"... Abstract RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation ..."
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Cited by 89 (1 self)
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Abstract RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras. 1
Face alignment by explicit shape regression
- in CVPR
, 2012
"... Abstract. We present a very efficient, highly accurate, “Explicit Shape Regression ” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly m ..."
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Cited by 82 (4 self)
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Abstract. We present a very efficient, highly accurate, “Explicit Shape Regression ” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods. To make the regression more effective and efficient, we design a two-level boosted regression, shape-indexed features and a correlation-based feature selection method. This combination enables us to learn accurate models from large training data in a short time (20 minutes for 2,000 training images), and run regression extremely fast in test (15 m-s for a 87 landmarks shape). Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency. 1.
Efficient Regression of General-Activity Human Poses from Depth Images
"... We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comp ..."
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Cited by 76 (5 self)
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We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comparison of several decision-tree training objectives. Key aspects of our work include: regression directly from the raw depth image, without the use of an arbitrary intermediate representation; applicability to general motions (not constrained to particular activities) and the ability to localize occluded as well as visible body joints. Experimental results demonstrate that our method produces state of the art results on several data sets including the challenging MSRC-5000 pose estimation test set, at a speed of about 200 frames per second. Results on silhouettes suggest broader applicability to other imaging modalities. 1.
Gool, L.: Real-time facial feature detection using conditional regression forests
, 2012
"... Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patche ..."
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Cited by 66 (8 self)
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Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patches and the location of feature points from the entire set of faces, conditional regression forest learn the relations conditional to global face properties. In our experiments, we use the head pose as a global property and demonstrate that conditional regression forests outperform regression forests for facial feature detection. We have evaluated the method on the challenging Labeled Faces in the Wild [20] database where close-to-human accuracy is achieved while processing images in real-time. 1.
Learning human activities and object affordances from rgb-d videos. IJRR
, 2013
"... such as making cereal and arranging objects in a room (see Fig. 9). For example, the making cereal activity consists of around 12 sub-activities on average, which includes reaching the pitcher, moving the pitcher to the bowl, and then pouring the milk into the bowl. This proves to be a very challeng ..."
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Cited by 59 (16 self)
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such as making cereal and arranging objects in a room (see Fig. 9). For example, the making cereal activity consists of around 12 sub-activities on average, which includes reaching the pitcher, moving the pitcher to the bowl, and then pouring the milk into the bowl. This proves to be a very challenging task given the variability across individuals in performing each sub-activity, and other environment induced conditions such as cluttered background and viewpoint changes. (See Fig. 2 for some examples.) In most previous works, object detection and activity recognition have been addressed as separate tasks. Only recently, some works have shown that modeling mutual context is beneficial (Gupta et al., 2009; Yao and Fei-Fei, 2010). The key idea in our work is to note that, in activity detection, it is sometimes more informative to know how an object is being used (associated affordances, Gibson, 1979) rather than knowing what the object is (i.e. the object category). For example, both chair and sofa might be categorized as ‘sittable, ’ and a cup might be categorized as both ‘drinkable ’ and ‘pourable. ’ Note that the affordances of an object change over time depending on its use, e.g., a pitcher may first be reachable, then movable and finally pourable. In addition to helping activity recognition, recognizing object affordances is important by itself because of their use in robotic applications (e.g., Kormushev et al., 2010; Jiang et al., 2012a; Jiang and Saxena, 2012). We propose a method to learn human activities by modarXiv:1210.1207v2
Whole-Home Gesture Recognition Using Wireless Signals Working Draft
"... Abstract – This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., WiFi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require lineof-sight and can traverse through walls, WiSee can enable whole-home gesture ..."
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Cited by 55 (2 self)
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Abstract – This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., WiFi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require lineof-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two-bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%. 1.
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
"... We address the problems of contour detection, bottomup grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundar ..."
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Cited by 48 (3 self)
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We address the problems of contour detection, bottomup grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundary detection and hierarchical segmentation that generalize the gP b − ucm approach of [2] by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We then turn to the problem of semantic segmentation and propose a simple approach that classifies superpixels into the 40 dominant object categories in NYUD2. We use both generic and class-specific features to encode the appearance and geometry of objects. We also show how our approach can be used for scene classification, and how this contextual information in turn improves object recognition. In all of these tasks, we report significant improvements over the state-of-the-art. 1.
Robust 3D action recognition with random occupancy patterns.
- In ECCV,
, 2012
"... Abstract. We study the problem of action recognition from depth sequences captured by depth cameras, where noise and occlusion are common problems because they are captured with a single commodity camera. In order to deal with these issues, we extract semi-local features called random occupancy pat ..."
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Cited by 48 (2 self)
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Abstract. We study the problem of action recognition from depth sequences captured by depth cameras, where noise and occlusion are common problems because they are captured with a single commodity camera. In order to deal with these issues, we extract semi-local features called random occupancy pattern (ROP) features, which employ a novel sampling scheme that effectively explores an extremely large sampling space. We also utilize a sparse coding approach to robustly encode these features. The proposed approach does not require careful parameter tuning. Its training is very fast due to the use of the high-dimensional integral image, and it is robust to the occlusions. Our technique is evaluated on two datasets captured by commodity depth cameras: an action dataset and a hand gesture dataset. Our classification results are superior to those obtained by the state of the art approaches on both datasets.
Streaming Hierarchical Video Segmentation
"... Abstract. The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet m ..."
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Cited by 45 (6 self)
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Abstract. The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method. 1
