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67
The Visual Analysis of Human Movement: A Survey
- Computer Vision and Image Understanding
, 1999
"... The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people ” is currently one of the most active application domains in compu ..."
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Cited by 743 (9 self)
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The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people ” is currently one of the most active application domains in computer vision. This survey identifies a number of promising applications and provides an overview of recent developments in this domain. The scope of this survey is limited to work on whole-body or hand motion; it does not include work on human faces. The emphasis is on discussing the various methodologies; they are grouped in 2-D approaches with or without explicit shape models and 3-D approaches. Where appropriate, systems are reviewed. We conclude with some thoughts about future directions. c ○ 1999 Academic Press 1.
Tracking People with Twists and Exponential Maps
, 1998
"... This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use of a novel mathematical technique, the product of exponential maps and twist motions, and its integra ..."
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Cited by 450 (5 self)
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This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use of a novel mathematical technique, the product of exponential maps and twist motions, and its integration into a differential motion estimation. This results in solving simple linear systems, and enables us to recover robustly the kinematic degrees-offreedom in noise and complex self occluded configurations. We demonstrate this on several image sequences of people doing articulated full body movements, and visualize the results in re-animating an artificial 3D human model. We are also able to recover and re-animate the famous movements of Eadweard Muybridge's motion studies from the last century. To the best of our knowledge, this is the first computer vision based system that is able to process such challenging footage and recover complex motions with such high accuracy.
Human Motion Analysis: A Review
- Computer Vision and Image Understanding
, 1999
"... Human motion analysis is receiving increasing at-tention from computer vision researchers. This inter-est is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man-machine interfaces, content-based image storage and retrieval, and video conferencing. T ..."
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Cited by 414 (10 self)
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Human motion analysis is receiving increasing at-tention from computer vision researchers. This inter-est is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man-machine interfaces, content-based image storage and retrieval, and video conferencing. This paper gives an overview of the various tasks involved in motion analysis of the human body. We focus on three major areas related to interpreting human motion: 1) motion analysis involving human body parts, 2) tracking of human motion wing single or multiple cameras, and 8) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints, and recovers the 3D structure of the human body using its 20 projections over a se-quence of images. Ilfacking human motion wing a single or multiple cameras focuses on higher-level pro-cessing, in which moving humans are tracked without identifying specific parts of the body structure. After successfully matching the moving human image)?om one frame to another in image sequences, understand-ing the human movements or activities comes natu-rally, which leads to our discussion of recognizing hu-man activities. The review is illustrated by ezamples. 1
Recent Developments in Human Motion Analysis
"... Visual analysis of human motion is currently one of the most active research topics in computer vision. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Human motion analysis concerns the ..."
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Cited by 264 (3 self)
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Visual analysis of human motion is currently one of the most active research topics in computer vision. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Human motion analysis concerns the detection, tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. This paper provides a comprehensive survey of research on computer vision based human motion analysis. The emphasis is on three major issues involved in a general human motion analysis system, namely human detection, tracking and activity understanding. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are discussed.
Reconstruction of articulated objects from point correspondences in a single uncalibrated image
- Computer Vision and Image Understanding
, 2000
"... This paper investigates the problem of recovering information about the con guration of an articulated object, such as a human gure, from point correspondences in a single image. Unlike previous approaches, the pro-posed reconstruction method does not assume that the imagery was ac-quired with a cal ..."
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Cited by 151 (0 self)
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This paper investigates the problem of recovering information about the con guration of an articulated object, such as a human gure, from point correspondences in a single image. Unlike previous approaches, the pro-posed reconstruction method does not assume that the imagery was ac-quired with a calibrated camera. An analysis is presented which demon-strates that there are a family of solutions to this reconstruction problem parameterized by a single variable. A simple and eective algorithm is proposed for recovering the entire set of solutions by considering the fore-shortening of the segments of the model in the image. Results obtained by applying this algorithm to real images are presented. Key Words: image based reconstruction, articulated gures, uncalibrated imagery 1.
Model-Based Estimation of 3D Human Motion with Occlusion Based on Active Multi-Viewpoint Selection
- In CVPR
, 1996
"... We present a new method for the 3D model-based tracking of human body parts. To mitigate the difficulties arising due to occlusion among body parts, we employ multiple calibrated cameras in a mutually orthogonal configuration. In addition, we develop criteria for a time varying active selection of a ..."
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Cited by 129 (8 self)
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We present a new method for the 3D model-based tracking of human body parts. To mitigate the difficulties arising due to occlusion among body parts, we employ multiple calibrated cameras in a mutually orthogonal configuration. In addition, we develop criteria for a time varying active selection of a set of cameras to track the motion of a particular human part. In particular, at every frame, each camera tracks a number of parts depending on the visibility of these parts and the observability of their predicted motion from the specific camera. To relate points on the occluding contours of the parts to points on their models we apply concepts from projective geometry. Then, within the physics-based framework we compute the generalized forces applied from the parts' occluding contours to model points of the body parts. These forces update the translational and rotational degrees of freedom of the model, such as to minimize the discrepancy between the sensory data and the estimated model s...
Estimating Articulated Human Motion With Covariance Scaled Sampling
- International Journal of Robotics Research
, 2003
"... We present a method for recovering 3D human body motion from monocular video sequences based on a robust image matching metric, incorporation of joint limits and non-self-intersection constraints, and a new sample-and-refine search strategy guided by rescaled cost-function covariances. Monocular 3D ..."
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Cited by 124 (10 self)
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We present a method for recovering 3D human body motion from monocular video sequences based on a robust image matching metric, incorporation of joint limits and non-self-intersection constraints, and a new sample-and-refine search strategy guided by rescaled cost-function covariances. Monocular 3D body tracking is challenging: besides the difficulty of matching an imperfect, highly flexible, self-occluding model to cluttered image features, realistic body models have at least 30 joint parameters subject to highly nonlinear physical constraints, and at least a third of these degrees of freedom are nearly unobservable in any given monocular image. For image matching we use a carefully designed robust cost metric combining robust optical flow, edge energy, and motion boundaries. The nonlinearities and matching ambiguities make the parameter-space cost surface multi-modal, ill-conditioned and highly nonlinear, so searching it is difficult. We discuss the limitations of CONDENSATION-like samplers, and describe a novel hybrid search algorithm that combines inflated-covariance-scaled sampling and robust continuous optimization subject to physical constraints and model priors. Our experiments on challenging monocular sequences show that robust cost modeling, joint and selfintersection constraints, and informed sampling are all essential for reliable monocular 3D motion estimation.
Twist Based Acquisition and Tracking of Animal and Human Kinematics
- INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2004
"... This paper demonstrates a new visual motion estimation technique that is able to recover high degree-offreedom articulated human body configurations in complex video sequences. We introduce the use and integration of a mathematical technique, the product of exponential maps and twist motions, into a ..."
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Cited by 97 (1 self)
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This paper demonstrates a new visual motion estimation technique that is able to recover high degree-offreedom articulated human body configurations in complex video sequences. We introduce the use and integration of a mathematical technique, the product of exponential maps and twist motions, into a differential motion estimation. This results in solving simple linear systems, and enables us to recover robustly the kinematic degrees-of-freedom in noise and complex self occluded configurations. A new factorization technique lets us also recover the kinematic chain model itself. We are able to track several human walk cycles, several wallaby hop cycles, and two walk cycels of the famous movements of Eadweard Muybridge's motion studies from the last century. To the best of our knowledge, this is the first computer vision based system that is able to process such challenging footage.
Towards Detection of Human Motion
- IN CVPR
, 2000
"... Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocula ..."
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Cited by 75 (9 self)
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Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocular image sequences; extraneous motions and occlusion may be present. We assume that we may not rely on segmentation, nor grouping and that the vision front-end is limited to observing the motion of key points and textured patches in between pairs of frames. We do not assume that we are able to track features for more than two frames. Our method is based on learning an approximate probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate and efficient.
Automatic Partitioning of High Dimensional Search Spaces associated with Articulated Body Motion Capture,”
- in Int. Conf. on Computer Vision and Pattern Recognition (CVPR),
, 2001
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