Human action recognition using fusion of multiview and deep features: an application to video surveillance

Khan, Muhammad Attique; Javed, Kashif; Khan, Sajid Ali; Saba, Tanzila; Habib, Usman; Khan, Junaid Ali; Abbasi, Aaqif Afzaal

doi:10.1007/s11042-020-08806-9

Cited by 125 publications

(60 citation statements)

References 34 publications

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“…However, the deep convolutional neural network is still inadequate for clothing style recognition. Khan et al [22,23] proposed the famous deep residual network ResNet. Compared with the traditional convolutional neural network, the deep residual network introduces a residual module into the network, which effectively alleviates the gradient disappearance of back propagation during network model training, thus solving the problems of difficult training and performance degradation in the deep network.…”

Section: Related Workmentioning

confidence: 99%

Multideep Feature Fusion Algorithm for Clothing Style Recognition

Wang

et al. 2021

Wireless Communications and Mobile Computing

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In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, which aims at focusing on clothing itself in the process of feature extraction procedure. Based on that, we propose a multideep feature fusion algorithm for clothing image style recognition. First, we use the improved target detection model to extract the global area, main part, and part areas of clothing, which constitute the image, so as to weaken the influence of the background and other interference factors. Then, the three parts were inputted, respectively, to improve ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch-normalized layer and the activation layer. Finally, the multicategory fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing style recognition, and is better than the traditional deep residual network-based methods.

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Section: Related Workmentioning

confidence: 99%

Multideep Feature Fusion Algorithm for Clothing Style Recognition

Wang

et al. 2021

Wireless Communications and Mobile Computing

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“…In recent times, deep learning has confirmed its supremacy for many computer vision and machine learning applications like action recognition [16], gait recognition [17,18], object detection [19,20], and many more [21][22][23]. For malware detection and classification, different researchers have applied deep learning and image processing techniques to accomplish high accuracy because of their ground-breaking capacity to learn the best features.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

et al. 2020

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As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code. In this work, we use distinctive pre-trained models (AlexNet and Inception-V3) for feature extraction. The purpose of deep convolutional neural network (CNN) feature extraction from two models is to improve the malware classifier accuracy, because both models have characteristics and qualities to extract different features. This technique produces a fusion of features to build a multimodal representation of malicious code that can be used to classify the grayscale images, separating the malware into 25 malware classes. The features that are extracted from malware images are then classified using different variants of support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and other classifiers. To improve the classification results, we also adopted data augmentation based on affine image transforms. The presented method is evaluated on a Malimg malware image dataset, achieving an accuracy of 99.3%, which makes it the best among the competing approaches.

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“…Analyzing someone's unique walking patterns, also, allows identifying them at larger distances [13]. The gait analysis has become an active research area for medical and assisted living applications [14], but also user identity verification biometric applications because of its robustness and usefulness in many domains such as clinical analysis, airports, forensic, bus stations, and bank surveillance systems [15,16]. Tracking and identification of subjects between different un-calibrated non-overlapping stationary CCTV cameras based on gait analysis have been shown in [17].…”

Section: Introductionmentioning

confidence: 99%

Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine

Khan

Kadry²,

Parwekar

et al. 2021

Complex Intell. Syst.

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Human gait analysis is a novel topic in the field of computer vision with many famous applications like prediction of osteoarthritis and patient surveillance. In this application, the abnormal behavior like problems in walking style is detected of suspected patients. The suspected behavior means assessments in terms of knee joints and any other symptoms that directly affected patients’ walking style. Human gait analysis carries substantial importance in the medical domain, but the variability in patients’ clothes, viewing angle, and carrying conditions, may severely affect the performance of a system. Several deep learning techniques, specifically focusing on efficient feature selection, have been recently proposed for this purpose, unfortunately, their accuracy is rather constrained. To address this disparity, we propose an aggregation of robust deep learning features in Kernel Extreme Learning Machine. The proposed framework consists of a series of steps. First, two pre-trained Convolutional Neural Network models are retrained on public gait datasets using transfer learning, and features are extracted from the fully connected layers. Second, the most discriminant features are selected using a novel probabilistic approach named Euclidean Norm and Geometric Mean Maximization along with Conditional Entropy. Third, the aggregation of the robust features is performed using Canonical Correlation Analysis, and the aggregated features are subjected to various classifiers for final recognition. The evaluation of the proposed scheme is performed on a publicly available gait image dataset CASIA B. We demonstrate that the proposed feature aggregation methodology, once used with the Kernel Extreme Learning Machine, achieves accuracy beyond 96%, and outperforms the existing works and several other widely adopted classifiers.

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Human action recognition using fusion of multiview and deep features: an application to video surveillance

Cited by 125 publications

References 34 publications

Multideep Feature Fusion Algorithm for Clothing Style Recognition

Multideep Feature Fusion Algorithm for Clothing Style Recognition

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine

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