Human gait classification using combined HMM &amp;amp; SVM hybrid classifier

Das, Deepjoy

doi:10.1109/edcav.2015.7060561

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…The second stage is classification. Various classifiers are attempted in gait recognition, such as nearest neighbor (NN) [3], [15], [16], support vector machine (SVM) [17]- [19] and CNN.…”

Section: Introductionmentioning

confidence: 99%

Investigation of robust gait recognition for different appearances and camera view angles

Wattanapanich

Wei

Petchkit

2021

IJECE

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A gait recognition framework is proposed to tackle the challenge of unknown camera view angles as well as appearance changes in gait recognition. In the framework, camera view angles are firstly identified before gait recognition. Two compact images, gait energy image (GEI) and gait modified Gaussian image (GMGI), are used as the base gait feature images. Histogram of oriented gradients (HOG) is applied to the base gait feature images to generate feature descriptors, and then a final feature map after principal component analysis (PCA) operations on the descriptors are used to train support vector machine (SVM) models for individuals. A set of experiments are conducted on CASIA gait database B to investigate how appearance changes and unknown view angles affect the gait recognition accuracy under the proposed framework. The experimental results have shown that the framework is robust in dealing with unknown camera view angles, as well as appearance changes in gait recognition. In the unknown view angle testing, the recognition accuracy matches that of identical view angle testing in gait recognition. The proposed framework is specifically applicable in personal identification by gait in a small company/organization, where unintrusive personal identification is needed.

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“…The second stage is classification. Various classifiers are attempted in gait recognition, such as nearest neighbor (NN) [3], [15], [16], support vector machine (SVM) [17]- [19] and CNN.…”

Section: Introductionmentioning

confidence: 99%

Investigation of robust gait recognition for different appearances and camera view angles

Wattanapanich

Wei

Petchkit

2021

IJECE

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“…A large number of studies related to human perception have used Johansson's point light display [1] [2]. Point-light (PL) display contains sufficient information to perceive actions [3], gender [4] [5] [6] [7] [8], emotions [9] [10] [11] [12] [13] [14] and to identify individuals [15] [16] [17] [18].…”

Section: Introductionmentioning

confidence: 99%

Human gait based gender identification system using Hidden Markov Model and Support Vector Machines

Das

Chakrabarty

2015

International Conference on Computing, Communication &Amp; Automation

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The paper presents an approach towards human gender recognition system. The Silhouettes from Center for Biometrics and Security Research (CASIA) gait database are segmented in order to identify major body points and to generate corresponding point-light display. The features such as two dimensional coordinates of major body points and joint angles are extracted from the point-light display. The features are classified using Hidden Markov Model (HMM) and Support Vector Machines (SVM). The study yields a recognition rate of 69.18% and 76.79% with 100 subject data using HMM and SVM respectively. There has been a significant improvement in recognition accuracy using joint angles as the features.

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“…The better description of the scene in Figure 12 may be a woman is walking; then a bike is in the background; later other humans are in the background. where 'then' and 'later' indicate the order of occurrences 8 . Figure 12 further elaborates the concept of unit in videos.…”

Section: A Woman Is Walking; a Woman Is Walking While A Bike Is In Thmentioning

confidence: 99%

“…Commonly observed actions, such as 'walking', 'running', 'standing', and 'sitting', can be identified. Human body is presented in the form of sticks to generate features such as torso, arm length and angle, leg angle and stride (8). Further Haar features are extracted and classifiers are trained to identify non-human objects (45).…”

Section: High Level Features Extraction From Videomentioning

confidence: 99%

Generating natural language tags for video information management

Khan

Gotoh

2017

Machine Vision and Applications

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This exploratory work is concerned with generation of natural language descriptions that can be used for video retrieval applications. It is a step ahead of keyword based tagging as it captures relations between keywords associated with videos. Firstly we prepare hand annotations consisting of descriptions for video segments crafted from a TREC Video dataset. Analysis of this data presents insights into human's interests on video contents. Secondly we develop a framework for creating smooth and coherent description of video streams. It builds on conventional image processing techniques that extract high level features from individual video frames. Natural language description is then produced based on high level features. Although feature extraction processes are erroneous at various levels, we explore approaches to putting them together to produce a coherent, smooth and well phrased description by incorporating spatial and temporal information. Evaluation is made by calculating ROUGE scores between human annotated and machine generated descriptions. Further we introduce a task based evaluation by human subjects which provides qualitative evaluation of generated descriptions.

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Human gait classification using combined HMM & SVM hybrid classifier

Cited by 10 publications

References 38 publications

Investigation of robust gait recognition for different appearances and camera view angles

Investigation of robust gait recognition for different appearances and camera view angles

Human gait based gender identification system using Hidden Markov Model and Support Vector Machines

Generating natural language tags for video information management

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