Improved strategy for human action recognition; experiencing a cascaded design

Khan, Muhammad Attique; Akram, Tallha; Sharif, Muhammad; Muhammad, Nazeer; Javed, Muhammad Awais; Naqvi, Syed Rameez

doi:10.1049/iet-ipr.2018.5769

Cited by 26 publications

(14 citation statements)

References 56 publications

(54 reference statements)

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“…Moreover, this type of process is time-consuming and takes up a lot of space, which later affects the entire system's computational time. Further, the main limitation of this process is that if a human silhouette is incorrectly detected, irrelevant features are extracted [23,24]. Researchers have tried to resolve these issues using feature reduction techniques such as principle component analysis (PCA) [25,26], linear discriminant analysis (LDA) [27], and improved PCA [28], among others [29,30].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

Saleem

Khan

Alhaisoni

et al. 2021

Sensors

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“…The PCA is used for the dimensionality reduction and provides improved efficiency in recognition. The few other methods are 16-layers CNN [17], fusion of features [18], weighted segmentation based approach [19], fusion of deep and handcrafted [19], and name a few more [20].…”

Section: Introductionmentioning

confidence: 99%

Video Analytics Framework for Human Action Recognition

Masood¹,

Khan²,

Alhaisoni³

et al. 2021

Computers, Materials &Amp; Continua

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Human action recognition (HAR) is an essential but challenging task for observing human movements. This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms. This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation, features reduction and selection framework. A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted. An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion. A custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and wellknown features. The features are exploited by a multi-class SVM for action identification. Comprehensive experimental results are undertaken on four action datasets, namely, Weizmann, KTH, Muhavi, and WVU multi-view. We achieved the recognition rate of 96.80%, 100%, 100%, and 100% respectively. Analysis reveals that the proposed action recognition approach is efficient and well accurate as compare to existing approaches.

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“…From simple to complex, the contents of human action recognition can be divided into three levels, namely, mobile vision, action vision, and behavior vision [8][9][10][11][12]. However, weakly correlated action frames are often poorly processed, and the current method for representation and information fusion cannot select desirable features.…”

Section: Introductionmentioning

confidence: 99%

Dance Action Recognition and Pose Estimation Based on Deep Convolutional Neural Network

Zhu¹,

Zhu²

2021

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Sports action recognition helps athletes correct their action range and standardize their poses. But it is not an easy task to recognize sports actions, due to the individual difference in action execution. Besides, the difficulty of action recognition increases with the diversity of actions and the complexity of background. The previous studies have not fully considered temporal changes, and failed to determine the exact staring point of actions. To solve the problem, this paper proposes a new method to recognize dance actions and estimate poses based on deep convolutional neural network (DCNN). Firstly, the authors presented full-effect expression of global and local features of dance actions, and derived an optimal model based on DeepPose. Next, a dance pose evaluation model was established based on time sequence segmentation network, and the sparse time sampling strategy was introduced to realize efficient and effective learning of the frame sequence of the whole video. Experimental results confirm the superiority of the full-effect expression of global and local features, and the effectiveness of the proposed model. The research results provide a reference for the application of deep learning (DL) in other scenarios of action recognition and pose estimation.

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Improved strategy for human action recognition; experiencing a cascaded design

Cited by 26 publications

References 56 publications

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

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

Video Analytics Framework for Human Action Recognition

Dance Action Recognition and Pose Estimation Based on Deep Convolutional Neural Network

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