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29
Learning Discriminative Representations from RGB-D Video Data
- PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2013
"... Recently, the low-cost Microsoft Kinect sensor, which can capture real-time high-resolution RGB and depth visual information, has attracted increasing attentions for a wide range of applications in computer vision. Existing techniques extract hand-tuned features from the RGB and the depth data separ ..."
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Recently, the low-cost Microsoft Kinect sensor, which can capture real-time high-resolution RGB and depth visual information, has attracted increasing attentions for a wide range of applications in computer vision. Existing techniques extract hand-tuned features from the RGB and the depth data separately and heuristically fuse them, which would not fully exploit the complementarity of both data sources. In this paper, we introduce an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGB-D video data for visual recognition tasks. We address this as an optimization problem using our proposed restricted graph-based genetic programming (RGGP) approach, in which a group of primitive 3D operators are first randomly assembled as graph-based combinations and then evolved generation by generation by evaluating on a set of RGB-D video samples. Finally the best-performed combination is selected as the (near-)optimal representation for a pre-defined task. The proposed method is systematically evaluated on a new hand gesture dataset, SKIG, that we collected ourselves and the public MSRDailyActivity3D dataset, respectively. Extensive experimental results show that our approach leads to significant advantages compared with state-of-the-art hand-crafted and machine-learned features.
Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition
"... Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in develop-ing methods for human gesture and action recognition from 3D skeletal data. A number of approaches have been pro-posed to extract representative features from 3D skeletal data, most commonl ..."
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Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in develop-ing methods for human gesture and action recognition from 3D skeletal data. A number of approaches have been pro-posed to extract representative features from 3D skeletal data, most commonly hard wired geometric or bio-inspired shape context features. We propose a hierarchial dynamic framework that first extracts high level skeletal joints fea-tures and then uses the learned representation for estimat-ing emission probability to infer action sequences. Current-ly gaussian mixture models are the dominant technique for modeling the emission distribution of hidden Markov mod-els. We show that better action recognition using skele-tal features can be achieved by replacing gaussian mixture models by deep neural networks that contain many layers of features to predict probability distributions over states of hidden Markov models. The framework can be easily ex-tended to include a ergodic state to segment and recognize actions simultaneously. 1.
A State of the Art Report on Kinect Sensor Setups in Computer Vision
"... Abstract. During the last three years after the launch of the Microsoft Kinect R ○ in the end-consumer market we have become witnesses of a small revolution in computer vision research towards the use of a standardized consumer-grade RGBD sensor for scene content retrieval. Beside classical localiza ..."
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Abstract. During the last three years after the launch of the Microsoft Kinect R ○ in the end-consumer market we have become witnesses of a small revolution in computer vision research towards the use of a standardized consumer-grade RGBD sensor for scene content retrieval. Beside classical localization and motion capturing tasks the Kinect has successfully been employed for the reconstruction of opaque and transparent objects. This report gives a comprehensive overview over the main publications using the Microsoft Kinect out of its original context as a decision-forest based motion-capturing tool. 1
Real-Time Multiple Human Perception with Color-Depth Cameras on a Mobile Robot
"... Abstract—The ability to perceive humans is an essential re-quirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability ..."
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Abstract—The ability to perceive humans is an essential re-quirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability of commercial color-depth cameras allow for the creation of a system that makes use of the depth dimension, thus enabling a robot to observe its environment and perceive in the 3-D space. Here we present a system for 3-D multiple human perception in real time from a moving robot equipped with a color-depth camera and a consumer-grade computer. Our approach reduces computation time to achieve real-time performance through a unique combination of new ideas and established techniques. We remove the ground and ceiling planes from the 3-D point cloud input to separate candidate point clusters. We introduce the novel information concept, depth of interest, which we use to identify candidates for detection, and that avoids the computationally expensive scanning-window methods of other approaches. We utilize a cascade of detectors to distinguish humans from objects, in which we make intelligent reuse of intermediary features in successive detectors to improve computation. Because of the high computational cost of some methods, we represent our candidate tracking algorithm with a decision directed acyclic graph, which allows us to use the most computationally intense techniques only where necessary. We detail the successful implementation of our novel approach on a mobile robot and examine its performance in scenarios with real-world challenges, including occlusion, robot motion, nonupright humans, humans leaving and reentering the field of view (i.e., the reidentification challenge), human-object and human-human interaction. We conclude with the observation that the incorporation of the depth information, together with the use of modern techniques in new ways, we are able to create an accurate system for real-time 3-D perception of humans by a mobile robot. Index Terms—3-D vision, depth of interest, human detection and tracking, human perception, RGB-D camera application.
3d reconstruction of body parts using rgb-d sensors: Challenges from a biomedical perspective
- in the Proceedings of the 5th International Conference and Exhibition on 3D Body Scanning Technologies (accepted
, 2014
"... The patient 3D model reconstruction plays an important role in applications such as surgery planning or computer-aided prosthesis design systems. Common methods use either expensive devices or require expert personnel which are not available in every clinic. Thus to make patient-specific modelling m ..."
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The patient 3D model reconstruction plays an important role in applications such as surgery planning or computer-aided prosthesis design systems. Common methods use either expensive devices or require expert personnel which are not available in every clinic. Thus to make patient-specific modelling more versatile, it is required to develop efficient methods together with feasible devices. Body parts such as head and torso present valid challenges with different degrees of complexity, especially because of the absence of relevant and abundant features. Considering Microsoft Kinect, it is a low-cost and widely available sensor, which has been successfully applied in medical applications. Since single depth-map acquired by Kinect is often incomplete and noisy, different approaches have been proposed to perform the reconstruction by merging multiple depth-maps, by registering single view point clouds generated form each point cloud. As human body is a non-rigid model, most of previous reconstruction methods using Kinect fail to perform accurate reconstruction since they do not address non-rigid surfaces. In this paper we present the challenges of using low-cost RGB-D sensors to reconstruct human body. Additionally, we analysed coarse registration stage to understand its impact on the quality of
Regularity Guaranteed Human Pose Correction
- In Computer Vision—ACCV 2014
, 2014
"... Abstract. Benefited from the advantages provided by depth sensors, 3D human pose estimation has become feasible. However, the curren-t estimation systems usually yield poor results due to severe occlusion and sensor noise in depth data. In this paper, we focus on a post-process step, pose correction ..."
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Abstract. Benefited from the advantages provided by depth sensors, 3D human pose estimation has become feasible. However, the curren-t estimation systems usually yield poor results due to severe occlusion and sensor noise in depth data. In this paper, we focus on a post-process step, pose correction, which takes the initial estimated poses as the input and deliver more reliable results. Although the regression based correc-tion approach [1] has shown its effectiveness in decreasing the estimated errors, it cannot guarantee the regularity of corrected poses. To address this issue, we formulate pose correction as an optimization problem, which combines the output of the regression model with a pose prior model learned on a pre-captured motion data set. By considering the complexity and the geometric property of the pose data, the pose pri-or is estimated by von Mises-Fisher distributions in subspaces following divide-and-conquer strategies. By introducing the pose prior into our op-timization framework, the regularity of the corrected poses is guaranteed. The experimental results on a challenging data set demonstrate that the proposed pose correction approach not only improves the accuracy, but also outputs more regular poses, compared to the-state-of-the-art. 1
Design and performance analysis of an indoor position tracking technique for smart rollators
- In Proc. of 4th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2013, Monbeliard, Belfrot
, 2013
"... Abstract—This paper presents a position tracking technique based on multisensor data fusion for rollators helping elderly people to move safely in large indoor spaces such as public buildings, shopping malls or airports. The proposed technique has been developed within the FP7 project DALi, and reli ..."
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Abstract—This paper presents a position tracking technique based on multisensor data fusion for rollators helping elderly people to move safely in large indoor spaces such as public buildings, shopping malls or airports. The proposed technique has been developed within the FP7 project DALi, and relies on an extended Kalman filter processing data from dead-reckoning sen-sors (i.e. encoders and gyroscopes), a short-range radio frequency identification (RFID) system and a front Kinect camera. As known, position tracking based on dead-reckoning sensors only is intrinsically affected by growing uncertainty. In order to keep such uncertainty within wanted boundaries, the position values are occasionally updated using a coarse-grained grid of low-cost passive RFID tags with known coordinates in a given map-based reference frame. Unfortunately, RFID tag detection does not provide any information about the orientation of the rollator. Therefore, a front camera detecting some markers on the walls is used to adjust direction. Of course, the data rate from both the RFID reader and the camera is not constant, as it depends on the actual user’s trajectory and on the distance between pairs of RFID tags and pairs of markers. Therefore, the average distance between tags and markers should be properly set to achieve a good trade-off between overall deployment costs and accuracy. In the paper, the results of a simulation-based performance analysis are reported in view of implementing the proposed localization and tracking technique in a real environment. Index Terms—Indoor localization, position tracking, Kalman filter, data fusion, performance evaluation I.
Citation:
, 2015
"... (2015) Self-organizing neural integration of pose-motion features for human action recognition. Front. Neurorobot. 9:3. doi: 10.3389/fnbot.2015.00003 Self-organizing neural integration of pose-motion features for human action recognition ..."
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(2015) Self-organizing neural integration of pose-motion features for human action recognition. Front. Neurorobot. 9:3. doi: 10.3389/fnbot.2015.00003 Self-organizing neural integration of pose-motion features for human action recognition
Fast MRF Optimization with Application to Depth Reconstruction
"... We describe a simple and fast algorithm for optimizing Markov random fields over images. The algorithm performs block coordinate descent by optimally updating a horizon-tal or vertical line in each step. While the algorithm is not as accurate as state-of-the-art MRF solvers on traditional benchmark ..."
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We describe a simple and fast algorithm for optimizing Markov random fields over images. The algorithm performs block coordinate descent by optimally updating a horizon-tal or vertical line in each step. While the algorithm is not as accurate as state-of-the-art MRF solvers on traditional benchmark problems, it is trivially parallelizable and pro-duces competitive results in a fraction of a second. As an application, we develop an approach to increasing the ac-curacy of consumer depth cameras. The presented algo-rithm enables high-resolution MRF optimization at multiple frames per second and substantially increases the accuracy of the produced range images. 1.
