Video Analytics Framework for Human Action Recognition

Masood, Usman Tariq Tariq; Khan, Muhammad Attique; Alhaisoni, Majed; Armghan, Ammar; Alenezi, Fayadh; Nam, Yunyoung; Akram, Tallha

doi:10.32604/cmc.2021.016864

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…For the multiperson target scene, the confidence is appropriately lowered, and a variety of calculation methods are introduced to adjust the confidence penalty. Finally, it analyzes which penalty function calculation method can better improve the recognition performance of the algorithm in dense human scenes and reduce the sensitivity of the model to the confidence threshold through experiments [28].…”

Section: Human Key Point Detection Based On Improved Mask Rcnnmentioning

confidence: 99%

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Hua-ying

2022

Mobile Information Systems

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With the improvement of computer computing power and storage capacity, the emergence of massive data makes the methods based on human action feature detection and recognition unable to meet people’s needs due to poor generalization ability. Based on the detection and recognition of human action features based on deep learning algorithms, a suitable neural network can be constructed to identify locked human action features from surveillance video and analyze whether it is a specific behavior. In this paper, a deep learning algorithm is proposed to optimize the detection of human action features, and a multiview reobservation fusion action recognition model of 3D pose is designed. Several factors affecting the recognition of human action features are analyzed, and a detailed summary is made from the detection environment. Experiments show that adding one or two layers of feature attention enhancement to the multiview observation fusion network can improve the accuracy by 1% to 3%. In this way, the model can integrate action features from multiple observation angles to judge actions and learn to find observation angles suitable for action recognition, thereby improving the performance of action recognition.

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Section: Human Key Point Detection Based On Improved Mask Rcnnmentioning

confidence: 99%

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Hua-ying

2022

Mobile Information Systems

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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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“…HAR is primarily carried out by extracting spatiotemporal interest points (STIP) in RGB video. Kovashka and Grauman [ 12 ] have proposed a human action recognition method based on hierarchical [ 13 ] model. This method combines HOG3D [ 14 ], HOG (histograms of oriented gradients), and HOF (histograms of optical flow) spatiotemporal domain descriptors and introduces a multicore learning model.…”

Section: Introductionmentioning

confidence: 99%

Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition

Tang

Tong

Zheng

et al. 2022

Computational Intelligence and Neuroscience

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The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.

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Video Analytics Framework for Human Action Recognition

Cited by 11 publications

References 32 publications

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

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

Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition

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