Computing View-normalized Body Parts Trajectories

Jean, Frédéric; Bergevin, Robert; Albu, Alexandra Branzan

doi:10.1109/crv.2007.19

Cited by 3 publications

(2 citation statements)

References 13 publications

(11 reference statements)

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“…In order to identify persons, Kale et al [ 40 ] introduced a gait recognition algorithm based on static body metrics that are collected from the walking across multiple views. A framework was presented by Jean et al [ 41 ] to compute and assess the view-normalized trajectories of the head and feet as seen in monocular videos. Hu et al [ 42 ] suggested using a unitary linear projection to reduce the dimensions of the original gait parameters that were taken from any view(s) and increase their capacity to differentiate between different views.…”

Section: Related Workmentioning

confidence: 99%

A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

Azhar

Ullah

et al. 2022

Sensors

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Gait-based gender classification is a challenging task since people may walk in different directions with varying speed, gait style, and occluded joints. The majority of research studies in the literature focused on gender-specific joints, while there is less attention on the comparison of all of a body’s joints. To consider all of the joints, it is essential to determine a person’s gender based on their gait using a Kinect sensor. This paper proposes a logistic-regression-based machine learning model using whole body joints for gender classification. The proposed method consists of different phases including gait feature extraction based on three dimensional (3D) positions, feature selection, and classification of human gender. The Kinect sensor is used to extract 3D features of different joints. Different statistical tools such as Cronbach’s alpha, correlation, t-test, and ANOVA techniques are exploited to select significant joints. The Coronbach’s alpha technique yields an average result of 99.74%, which indicates the reliability of joints. Similarly, the correlation results indicate that there is significant difference between male and female joints during gait. t-test and ANOVA approaches demonstrate that all twenty joints are statistically significant for gender classification, because the p-value for each joint is zero and less than 1%. Finally, classification is performed based on the selected features using binary logistic regression model. A total of hundred (100) volunteers participated in the experiments in real scenario. The suggested method successfully classifies gender based on 3D features recorded in real-time using machine learning classifier with an accuracy of 98.0% using all body joints. The proposed method outperformed the existing systems which mostly rely on digital images.

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Section: Related Workmentioning

confidence: 99%

A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

Azhar

Ullah

et al. 2022

Sensors

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“…This paper is an extension of the work presented in [24]. A new evaluation method is proposed to assess the performance of the normalization algorithm.…”

Section: Introductionmentioning

confidence: 97%

Computing and evaluating view-normalized body part trajectories

Jean

Bergevin

Albu

2009

Image and Vision Computing

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a b s t r a c tThis paper proposes an approach to compute and evaluate view-normalized body part trajectories of pedestrians from monocular video sequences. The proposed approach uses the 2D trajectories of both feet and of the head extracted from the tracked silhouettes. On that basis, it segments the walking trajectory into piecewise linear segments. Finally, a normalization process is applied to head and feet trajectories over each obtained straight walking segment. View normalization makes head and feet trajectories appear as if seen from a fronto-parallel viewpoint. The latter is assumed to be optimal for gait modeling and identification purposes. The proposed approach is fully automatic as it requires neither manual initialization nor camera calibration. An extensive experimental evaluation of the proposed approach confirms the validity of the normalization process.

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