Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Khan, Awais; Khan, Muhammad Attique; Javed, Muhammad Awais; Alhaisoni, Majed; Tariq, Usman; Kadry, Seifedine; Choi, Jung-In; Nam, Yunyoung

doi:10.32604/cmc.2022.018270

Cited by 26 publications

(20 citation statements)

References 27 publications

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“…A study using three angles (0, 18 and 180) of the CASIA B dataset was presented in [9]. The data is normalized before being fed into two pre-trained models: ResNet101 and InceptionV3.…”

Section: Related Workmentioning

confidence: 99%

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Vasudevan¹,

Mattins²,

Srivarshan³

et al. 2023

Computers, Materials &Amp; Continua

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Gait refers to a person's particular movements and stance while moving around. Although each person's gait is unique and made up of a variety of tiny limb orientations and body positions, they all have common characteristics that help to define normalcy. Swiftly identifying such characteristics that are difficult to spot by the naked eye, can help in monitoring the elderly who require constant care and support. Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait. It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis. Gait images made publicly available by the Chinese Academy of Sciences (CASIA) Gait Database was used in this study. After evaluating using the CASIA B and C datasets, this paper proposes a Convolutional Neural Network (CNN) and a CNN Long Short-Term Memory Network (CNN-LSTM) model for classifying the gait silhouette images. Transfer learning models such as MobileNetV2, InceptionV3, Visual Geometry Group (VGG) networks such as VGG16 and VGG19, Residual Networks (ResNet) like the ResNet9 and ResNet50, were used to compare the efficacy of the proposed models. CNN proved to be the best by achieving the highest accuracy of 94.29%. This was followed by ResNet9 and CNN-LSTM, which arrived at 93.30% and 87.25% accuracy, respectively.

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“…A study using three angles (0, 18 and 180) of the CASIA B dataset was presented in [9]. The data is normalized before being fed into two pre-trained models: ResNet101 and InceptionV3.…”

Section: Related Workmentioning

confidence: 99%

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Vasudevan¹,

Mattins²,

Srivarshan³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…We review a few of them as shown in Figure 2 , namely [ 13 , 14 ], which worked on the identification of a person in crowded environments with low-resolution images as the source of input. Some other works are [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. However, Table 1 depicts the details of the environment constraints, the tools and techniques used, the methodology, the dataset and performance used by various researchers.…”

Section: Related Workmentioning

confidence: 99%

A Novel Integration of Face-Recognition Algorithms with a Soft Voting Scheme for Efficiently Tracking Missing Person in Challenging Large-Gathering Scenarios

Nadeem

Ashraf

Rizwan

et al. 2022

Sensors

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The probability of losing vulnerable companions, such as children or older ones, in large gatherings is high, and their tracking is challenging. We proposed a novel integration of face-recognition algorithms with a soft voting scheme, which was applied, on low-resolution cropped images of detected faces, in order to locate missing persons in a challenging large-crowd gathering. We considered the large-crowd gathering scenarios at Al Nabvi mosque Madinah. It is a highly uncontrolled environment with a low-resolution-images data set gathered from moving cameras. The proposed model first performs real-time face-detection from camera-captured images, and then it uses the missing person’s profile face image and applies well-known face-recognition algorithms for personal identification, and their predictions are further combined to obtain more mature prediction. The presence of a missing person is determined by a small set of consecutive frames. The novelty of this work lies in using several recognition algorithms in parallel and combining their predictions by a unique soft-voting scheme, which in return not only provides a mature prediction with spatio-temporal values but also mitigates the false results of individual recognition algorithms. The experimental results of our model showed reasonably good accuracy of missing person’s identification in an extremely challenging large-gathering scenario.

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“…HAR has emerged as an impactful research area in CV from the last decade [ 22 ]. It is based on important applications such as visual surveillance [ 23 ], robotics, biometrics [ 24 , 25 ], and smart healthcare centers to name a few [ 26 , 27 ]. Several researchers of computer vision developed techniques using machine learning [ 28 ] for HAR.…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Khan

Alhaisoni

et al. 2021

Sensors

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Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art.

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Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Cited by 26 publications

References 27 publications

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

A Novel Integration of Face-Recognition Algorithms with a Soft Voting Scheme for Efficiently Tracking Missing Person in Challenging Large-Gathering Scenarios

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

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