3,166 research outputs found
Comparing Different Template Features for Recognizing People by Their Gait
No full textTo recognize people by their gait from a sequence of images, we have proposed a statistical approach which combined eigenspace transformation (EST) with canonical space transformation (CST) for feature transformation of spatial templates. This approach is used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. Good recognition rates have been achieved. Here, we incorporate temporal information from optical flows into three kinds of temporal templates and use them as features for gait recognition in addition to the spatial templates. The recognition performance for four kinds of template features has been evaluated in this paper. Experimental results show that spatial templates, horizontal-flow templates and the combined horizontal-flow and vertical-flow templates are better than vertical-flow templates for gait recognition
Recognizing humans by gait using a statistical approach for temporal templates
No full textIn this paper, we propose a new approach which combines canonical space transformation (CST) with the eigenspace transformation (EST) for feature extraction of temporal templates in a gait sequence. Eigenspace transformation has been demonstrated to be a potent metric in automatic face recognition and gait analysis, but without using data analysis to increase classification capability. Our method can be used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. Each temporal template is projected from high-dimensional image space to a single point in low-dimensional canonical space. In this new space the recognition of human gait by template matching becomes much faster and simpler. Experimental results for human gait analysis show this method is superior to eigenspace representation. As such, the combination of EST and CST is shown to be of considerable advantage in an emerging new biometric
Canonical Space Representation for Recognizing Humans by Gait and Face
No full textEigenspace transformation (EST) based on Principal Component Analysis (PCA) has been demonstrated to be a potent metric in face recognition and gait analysis, but without using data analysis to increase classification capability. In this paper, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA) with the eigenspace transformation for feature extraction. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences and face classes simultaneously. Each image template is projected from high-dimensional image space to a single point in low-dimensional canonical space. In this new space the recognition of human gait and faces becomes much simpler. Experimental results for human gait analysis and face recognition show this method is superior to use EST or CST alone. As such, the combination of PCA and CA is shown to be of considerable advantage in an emerging new biometric
A Statistical Approach for Recognizing Humans by Gait using Spatial-Temporal Templates
No full textIn order to tackle the problem of recognizing humans by gait, we use an approach which combines eigenspace transformation (EST) with canonical space transformation (CST) for feature extraction of spatial templates from a gait sequence. Our proposed method can be used to reduce data dimensionality and to optimize the class separability of different gait sequences simultaneously. In this paper, we propose a new feature - temporal templates , and an extended feature which combines spatial and temporal templates for recognition. By incorporating spatial and temporal information into an extended feature vector in the canonical space, gait recognition becomes more robust and accurate than using any single feature alone
Recognising Humans by Gait via Parametric Canonical Space
No full textEigenspace transformation (EST) based on Principal Component Analysis (PCA) has been demonstrated to be a potent metric in gait analysis, but without using data analysis to increase classification capability. In this paper, we propose a new approach which combines canonical space transformation (CST) based on Canonical Analysis (CA), with the eigenspace transformation. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences and face classes simultaneously. Each image template is projected from high-dimensional image space to a single point in low-dimensional canonical space. In this new space the recognition of human gait becomes much simpler. Experimental results for human gait analysis show this method is superior to the eigenspace representation. The comparison of EST, CST and our approach is also shown in the results. As such, the combination of EST and CST is shown to be of considerable advantage in an emerging new biometric
Human Gait Recognition in Canonical Space Using Temporal Templates
No full textThis paper describes a system for automatic gait recognition without segmentation of particular body parts. Eigenspace transformation (EST) has already proved useful for several tasks including face recognition, gait analysis, etc. However, EST is optimal in dimensionality reduction by maximising the total scatter of all classes but is not optimal for class separability. In this paper, a statistical approach which combines EST with canonical space transformation (CST) is proposed for gait recognition using temporal templates from a gait sequence as features. This method can be used to reduce data dimensionality and to optimise the class separability of different gait sequences simultaneously. Incorporating temporal information from optical-flow changes between two consecutive spatial templates, each temporal template extracted from computation of optical flow is projected from a high-dimensional image space to a single point in a low-dimensional canonical space. Using template matching, recognition of human gait becomes much faster and simpler in this new space. As such, the combination of EST and CST is shown to be of considerable potential in an emerging new biometric
Visual Surveillance and Tracking of Humans by Face and Gait Recognition
No full textIncreased emphasis on automated real time intelligent surveillance system has led to the need to identify and track people in complex environments. Independent features such as face, gait provide valuable clues as to identity, which coupled with data fusion and tracking algorithms offer a potential solution to this problem. In this paper we address the first problem of recognizing humans in real time, data fusion and tracking will be performed by neurofuzzy state estimators. A new approach which combines eigenspace transformation with canonical space transformation is proposed here. This method can be used to reduce data dimensionality and to optimize the class separability of different classes simultaneously
Mt. Borah
No full textA mountain rises above some wooded foothills. Description reads: ""Telephoto view of Mt. Borah (12,655 ft. elevation) highest mountain in Idaho, taken from Grazing Service CCC Camp Chilly #111. Forest: Challis, State: Idaho, Date: 7/1940, Author: P.S. Bieler""
UAVSAR Vertical Velocity Rate Map for the Sacramento-San Joaquin Delta (2009-2015)
No full textProvided are the dat files for the UAVSAR rate map and corresponding uncertainties. The rate map shows vertical velocity values for the Sacramento-San Joaquin Delta with negative values representing subsidence. See Bekaert, D. P., Jones, C. E., An, K., & Huang, M.-H. (2019). Exploiting UAVSAR for a comprehensive analysis of subsidence in the Sacramento Delta. Remote Sensing of Environment, 220, 124–134. doi: 10.1016/j.rse.2018.10.023 for more information on how this data was prepared.Related Publication:
Exploiting UAVSAR for a comprehensive analysis of subsidence in the Sacramento Delta
David P.S. Bekaert JPL-Caltech
Remote Sensing of Environment
2018-11-06
https://doi.org/10.1016/j.rse.2018.10.023
engContact person: Victoria Bennett [email protected]
Hyndman Peak
No full textA mountain is visible across a valley and between two hills. Description reads: ""Hyndman Peak (12,078 ft. elevation) as seen from upper Big Lost River near Kane Creek on Forest Road to Ketchum. Forest: Challis, State: Idaho, Date: 7/1940, Author: P.S. Bieler""
- …
