Results 1 - 10
of
252
People detection in RGB-D data
- In IEEE/RSJ Int. Conf. on
, 2011
"... Abstract — People detection is a key issue for robots and intelligent systems sharing a space with people. Previous works have used cameras and 2D or 3D range finders for this task. In this paper, we present a novel people detection approach for RGB-D data. We take inspiration from the Histogram of ..."
Abstract
-
Cited by 26 (2 self)
- Add to MetaCart
(Show Context)
Abstract — People detection is a key issue for robots and intelligent systems sharing a space with people. Previous works have used cameras and 2D or 3D range finders for this task. In this paper, we present a novel people detection approach for RGB-D data. We take inspiration from the Histogram of Oriented Gradients (HOG) detector to design a robust method to detect people in dense depth data, called Histogram of Oriented Depths (HOD). HOD locally encodes the direction of depth changes and relies on an depth-informed scale-space search that leads to a 3-fold acceleration of the detection process. We then propose Combo-HOD, a RGB-D detector that probabilistically combines HOD and HOG. The experiments include a comprehensive comparison with several alternative detection approaches including visual HOG, several variants of HOD, a geometric person detector for 3D point clouds, and an Haar-based AdaBoost detector. With an equal error rate of 85 % in a range up to 8m, the results demonstrate the robustness of HOD and Combo-HOD on a real-world data set collected with a Kinect sensor in a populated indoor environment. I.
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them
- INT J COMPUT VIS
, 2013
"... The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible throu ..."
Abstract
-
Cited by 25 (6 self)
- Add to MetaCart
The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this
Non-Sequential Structure from Motion
"... Prior work on multi-view structure from motion is dominated by sequential approaches starting from a single twoview reconstruction, then adding new images one by one. In contrast, we propose a non-sequential methodology based on rotational consistency and robust estimation using convex optimization. ..."
Abstract
-
Cited by 18 (1 self)
- Add to MetaCart
(Show Context)
Prior work on multi-view structure from motion is dominated by sequential approaches starting from a single twoview reconstruction, then adding new images one by one. In contrast, we propose a non-sequential methodology based on rotational consistency and robust estimation using convex optimization. The resulting system is more robust with respect to (i) unreliable two-view estimations caused by short baselines, (ii) repetitive scenes with locally consistent structures that are not consistent with the global geometry and (iii) loop closing as errors are not propagated in a sequential manner. Both theoretical justifications and experimental comparisons are given to support these claims. 1 1.
Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A Survey
, 2013
"... ..."
S.: Evaluation of colour models for computer vision using cluster validation techniques
- In: (Accepted) RoboCup 2012: Robot Soccer World Cup XVI (LNAI
, 2013
"... Abstract. Computer vision systems frequently employ colour segmen-tation as a step of feature extraction. This is particularly crucial in an environment where important features are colour-coded, such as robot soccer. This paper describes a method for determining an appropriate colour model by measu ..."
Abstract
-
Cited by 9 (8 self)
- Add to MetaCart
(Show Context)
Abstract. Computer vision systems frequently employ colour segmen-tation as a step of feature extraction. This is particularly crucial in an environment where important features are colour-coded, such as robot soccer. This paper describes a method for determining an appropriate colour model by measuring the compactness and separation of clusters produced by a k-means algorithm. RGB, HSV, YCbCr and CIE L*a*b* colour models are assessed for a selection of artificial and real images, utilising an implementation of the Dunn’s-based cluster validation index. The effectiveness of the method is assessed by qualitatively comparing the relative correctness of the segmentation to the results of the cluster validation. Results demonstrate there is a significant variation in segmen-tation quality among colour spaces, and that YCbCr is the best choice for the DARwIn-OP platform tested.
Time-of-Flight Cameras and Microsoft Kinect
- SpringerBriefs in Electrical and Computer Engineering
, 2012
"... To my father Umberto, who has continuously stimulated my interest for research To Marco, who left us too early leaving many beautiful remembrances and above all the dawn of a new small life To my father Gino, professional sculptor, to whom I owe all my work about 3D ..."
Abstract
-
Cited by 9 (4 self)
- Add to MetaCart
(Show Context)
To my father Umberto, who has continuously stimulated my interest for research To Marco, who left us too early leaving many beautiful remembrances and above all the dawn of a new small life To my father Gino, professional sculptor, to whom I owe all my work about 3D
Fused sparsity and robust estimation for linear models with unknown variance
- In NIPS
, 2012
"... with unknown variance ..."
(Show Context)
A.: A novel approach to ball detection for humanoid robot soccer
- In: Advances in Artificial Intelligence (LNAI 7691
, 2012
"... Abstract. The ability to accurately track a ball is a critical issue in humanoid robot soccer, made difficult by processor limitations and re-sultant inability to process all available data from a high-definition im-age. This paper proposes a computationally efficient method of deter-mining position ..."
Abstract
-
Cited by 8 (7 self)
- Add to MetaCart
(Show Context)
Abstract. The ability to accurately track a ball is a critical issue in humanoid robot soccer, made difficult by processor limitations and re-sultant inability to process all available data from a high-definition im-age. This paper proposes a computationally efficient method of deter-mining position and size of balls in a RoboCup environment, and com-pares the performance to two common methods: one utilising Levenberg-Marquardt least squares circle fitting, and the other utilising a circular Hough transform. The proposed method is able to determine the position of a non-occluded tennis ball with less than 10 % error at a distance of 5 meters, and a half-occluded ball with less than 20 % error, overall outper-forming both compared methods whilst executing 300 times faster than the circular Hough transform method. The proposed method is described fully in the context of a colour based vision system, with an explanation of how it may be implemented independent of system paradigm. An ex-tension to allow tracking of multiple balls utilising unsupervised learning and internal cluster validation is described.
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
"... Abstract—Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifi ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
(Show Context)
Abstract—Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that the above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios. Index Terms—Moving object detection, low-rank modeling, Markov Random Fields, motion segmentation Ç 1
