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111
Wallflower: Principles and Practice of Background Maintenance
, 1999
"... Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three-component system for background maintenance: the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills i ..."
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Cited by 477 (1 self)
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Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three-component system for background maintenance: the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and swaps in better approximations of the background. We compare our system with 8 other background subtraction algorithms. Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur. Finally, we analyze the experimental results and propose
A system for learning statistical motion patterns
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of th ..."
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Cited by 119 (1 self)
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permission from the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. © 2006 IEEE. Copyright and all rights therein are retained by authors or by other copyright holders. All persons downloading this information are expected to adhere to the terms and constraints invoked by copyright. This document or any part thereof may not be reposted without the explicit permission of the copyright holder. Citation for this copy:
Vision-Based Gesture Recognition: A Review
- Lecture Notes in Computer Science
"... The use of gesture as a natural interface serves as a motivating force for research in modeling, analyzing and recognition of gestures. In particular, human computer intelligent interaction needs vision-based gesture recognition, which involves many interdisciplinary studies. A survey on recent visi ..."
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Cited by 111 (1 self)
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The use of gesture as a natural interface serves as a motivating force for research in modeling, analyzing and recognition of gestures. In particular, human computer intelligent interaction needs vision-based gesture recognition, which involves many interdisciplinary studies. A survey on recent vision-based gesture recognition approaches is given in this paper. We shall review methods of static hand posture and temporal gesture recognition. Several application systems of gesture recognition are also described in this paper. We conclude with some thoughts about future research directions.
A Survey of Vision-Based Methods for Action Representation, Segmentation and Recognition
, 2011
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3D hand pose reconstruction using specialized mappings
- In Proc. International Conf. on Computer Vision (ICCV), Vol.1
, 2001
"... A system for recovering 3D hand pose from monocu-lar color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to likely 3D hand poses. The SMA’s fundamental components are a set of specialized forwa ..."
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Cited by 90 (10 self)
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A system for recovering 3D hand pose from monocu-lar color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to likely 3D hand poses. The SMA’s fundamental components are a set of specialized forward mapping functions, and a single feedback matching function. The forward functions are estimated directly from training data, which in our case are examples of hand joint configurations and their corre-sponding visual features. The joint angle data in the train-ing set is obtained via a CyberGlove, a glove with 22 sen-sors that monitor the angular motions of the palm and fin-gers. In training, the visual features are generated using a computer graphics module that renders the hand from ar-bitrary viewpoints given the 22 joint angles. The viewpoint is encoded by two real values, therefore 24 real values rep-resent a hand pose. We test our system both on synthetic sequences and on sequences taken with a color camera. The system automatically detects and tracks both hands of the user, calculates the appropriate features, and estimates the 3D hand joint angles and viewpoint from those features. Results are encouraging given the complexity of the task. 1
Robust hand detection
- In International Conference on Automatic Face and Gesture Recognition (to appear), Seoul, Korea
, 2004
"... Vision-based hand gesture interfaces require fast and extremely robust hand detection. Here, we study view-specific hand posture detection with an object recognition method recently proposed by Viola and Jones. Training with this method is computationally very expensive, prohibiting the evaluation o ..."
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Cited by 83 (7 self)
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Vision-based hand gesture interfaces require fast and extremely robust hand detection. Here, we study view-specific hand posture detection with an object recognition method recently proposed by Viola and Jones. Training with this method is computationally very expensive, prohibiting the evaluation of many hand appearances for their suitability to detection. As one contribution of this paper, we present a frequency analysis-based method for instantaneous estimation of class separability, without the need for any training. We built detectors for the most promising candidates, their receiver operating characteristics confirming the estimates. Next, we found that classification accuracy increases with a more expressive feature type. As a third contribution, we show that further optimization of training parameters yields additional detection rate improvements. In summary, we present a systematic approach to building an extremely robust hand appearance detector, providing an important step towards easily deployable and reliable vision-based hand gesture interfaces. 1
Motion Segmentation and Pose Recognition with Motion History Gradients
- Machine Vision and Applications
, 2000
"... This paper uses a simple method for representing motion in successively layered silhouettes that directly encode system time termed the timed Motion History Image (tMHI). This representation can be used to both (a) determine the current pose of the object and to (b) segment and measure the motions i ..."
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Cited by 76 (5 self)
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This paper uses a simple method for representing motion in successively layered silhouettes that directly encode system time termed the timed Motion History Image (tMHI). This representation can be used to both (a) determine the current pose of the object and to (b) segment and measure the motions induced by the object in a video scene. These segmented regions are not “motion blobs”, but instead motion regions naturally connected to the moving parts of the object of interest. This method may be used as a very general gesture recognition “toolbox”. We use it to recognize waving and overhead clapping motions to control a music synthesis program. 1. Introduction and Related
Fast 2D hand tracking with flocks of features and multi-cue integration
- In IEEE Workshop on Real-Time Vision for Human-Computer Interaction (at CVPR
, 2004
"... This paper introduces “Flocks of Features, ” a fast tracking method for non-rigid and highly articulated objects such as hands. It combines KLT features and a learned foreground color distribution to facilitate 2D position tracking from a monocular view. The tracker’s benefits lie in its speed, its ..."
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Cited by 62 (3 self)
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This paper introduces “Flocks of Features, ” a fast tracking method for non-rigid and highly articulated objects such as hands. It combines KLT features and a learned foreground color distribution to facilitate 2D position tracking from a monocular view. The tracker’s benefits lie in its speed, its robustness against background noise, and its ability to track objects that undergo arbitrary rotations and vast and rapid deformations. We demonstrate tracker performance on hand tracking with a non-stationary camera in unconstrained indoor and outdoor environments. The tracker yields over threefold improvement over a CamShift tracker in terms of the number of frames tracked before the target was lost, and often more than one order of magnitude improvement in terms of the fractions of particular test sequences tracked successfully. 1.
