A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

Afza, Farhat; Khan, Muhammad Attique; Sharif, Muhammad; Kadry, Seifedine; Manogaran, Gunasekaran; Saba, Tanzila; Ashraf, Imran; Damaševičius, Robertas

doi:10.1016/j.imavis.2020.104090

Cited by 115 publications

(52 citation statements)

References 23 publications

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“…The significance of multifaceted applications of hand gestures has increased in the industry and research fields [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Computer vision, pattern recognition, and human-computer interaction (HCI) are among the popular areas that involve 3D hand-gesture modeling [ 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Mishra

Kim

Cha

et al. 2021

Sensors

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An authorized traffic controller (ATC) has the highest priority for direct road traffic. In some irregular situations, the ATC supersedes other traffic control. Human drivers indigenously understand such situations and tend to follow the ATC; however, an autonomous vehicle (AV) can become confused in such circumstances. Therefore, autonomous driving (AD) crucially requires a human-level understanding of situation-aware traffic gesture recognition. In AVs, vision-based recognition is particularly desirable because of its suitability; however, such recognition systems have various bottlenecks, such as failing to recognize other humans on the road, identifying a variety of ATCs, and gloves in the hands of ATCs. We propose a situation-aware traffic control hand-gesture recognition system, which includes ATC detection and gesture recognition. Three-dimensional (3D) hand model-based gesture recognition is used to mitigate the problem associated with gloves. Our database contains separate training and test videos of approximately 60 min length, captured at a frame rate of 24 frames per second. It has 35,291 different frames that belong to traffic control hand gestures. Our approach correctly recognized traffic control hand gestures; therefore, the proposed system can be considered as an extension of the operational domain of the AV.

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Section: Methodsmentioning

confidence: 99%

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Mishra

Kim

Cha

et al. 2021

Sensors

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“…Deep learning showed much success in the area of machine learning in the past two decades. Well known applications of deep learning include visual surveillance, biometrics, and medicine, among others [44,45]. Biometrics is an important application and much research attends to this field [46].…”

Section: Related Workmentioning

confidence: 99%

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Saleem

Khan

Alhaisoni

et al. 2021

Sensors

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Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.

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“…In previous decades, different techniques have been used for HAR, such as computer vision methods [7][8][9] that use cameras to track human motion and actions, and wearable devices that should be carried by users, such as wearable sensors [10], smartwatches [11], and smartphones [12,13]. Additionally, there are other techniques, such as environment installed sensors [14], and WiFi signals, which include three techniques, namely received signal strength [15], channel state information [16], and WiFi radar (micro-Doppler radar) [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

Helmi

Al-qaness

Dahou

et al. 2021

Entropy

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Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.

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A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

Cited by 115 publications

References 23 publications

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

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