Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods

Parashar, Anubha; Shekhawat, Rajveer Singh; Ding, Weiping; Rida, Imad

doi:10.1016/j.neucom.2022.07.002

Cited by 29 publications

(6 citation statements)

References 150 publications

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“…The program is made in separate modules, that promotes adaptability. A simple GUI is implemented so that this application can be used by people with no technical or coding knowledge, as the GUI provides simple explanation and implements the working at the click of a button [38].…”

Section: Discussionmentioning

confidence: 99%

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A robust covariate‐invariant gait recognition based on pose features

Parashar

Shekhawat

2022

IET Biometrics

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Gait recognition uses video of human gait processed by computer vision methods to identify people based on walking style. The complexity introduced by covariates makes the previous methods less efficient and inaccurate. This study proposes an approach based on pose features to attempt gait recognition of people with an overcoat, carrying objects, or other covariates. It aims to estimate human locomotion using Convolutional Neural Networks. Gathering video data, extracting video frames in a particular order, posture estimation for each frame, using multilayer RNN for gait recognition from the pose, and obtaining one‐dimensional object vectors, are all critical steps. Furthermore, these one‐dimensional identification vectors are stored in a data set along with the name of the person walking in the video. The proposed data set is used to train a classification model to predict the person in a new video by first processing it to get its identification vector and then to use it as a test case in the classification model. A graphical user interface was also developed so that anyone with no programming or technical experience can easily use the tool. The developed application does everything for gait detection from mp4 videos by obtaining the identification vectors and saving them into the data set. Using this application, one can quickly identify the person walking in a video. The results obtained offered an accuracy from 60.88% to 95.23%.

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Section: Discussionmentioning

confidence: 99%

“…Biometrics have their some inherent advantages as compared to manual watch. It can be utilised for identification of criminals, present a superior level of security than usual means of authentication [38]. These systems can be integrated in surveillance systems by embedding biometric feature recognition of gait.…”

Section: Introductionmentioning

confidence: 99%

A robust covariate‐invariant gait recognition based on pose features

Parashar

Shekhawat

2022

IET Biometrics

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“…It means that the appearance features of the same object will be very different in cameras with different perspectives. In the stage of vehicle tracking, there are often problems such as large intra-class variability and inter-class similarity [8]. To solve these problems, most MOMCT methods follow the detection and tracking paradigm: firstly, a set of detection is generated independently for each video frame shot by the camera; secondly, these detections are linked together by similarity measurement to generate a continuous trajectory.…”

Section: Introductionmentioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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“…The aforementioned research example highlights a common trend: as the number of classes increases, there is a notable decrease in identification accuracy. This phenomenon is natural due to the increasing challenge of intra-class variation with the increasing number of recognized butterfly species [24]. The number of classes tested in the aforementioned studies is still significantly lower than the total number of butterfly species.…”

Section: Introductionmentioning

confidence: 99%

Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation

Adityawan,

Farroq,

Santosa

et al. 2023

J. Comput. Theor. Appl.

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Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.

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Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods

Cited by 29 publications

References 150 publications

A robust covariate‐invariant gait recognition based on pose features

A robust covariate‐invariant gait recognition based on pose features

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation

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