A resource conscious human action recognition framework using 26-layered deep convolutional neural network

Khan, Muhammad Attique; Zhang, Yudong; Khan, Sajid Ali; Attique, Muhammad; Rehman, Amjad; Seo, Sanghyun

doi:10.1007/s11042-020-09408-1

Cited by 72 publications

(51 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, considered the relations among 2 deep features, deep feature relation layers are presented for adjusting the DL network parameters on the basis of relation judgments. Khan et al [17] developed a novel twenty six layered CNN framework for precise HAR. The feature is extracted from the FC and global average pooling layers and merged with a presented entropy based method.…”

Section: Related Workmentioning

confidence: 99%

Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

Al‐Wesabi¹,

Albraikan

Hilal

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In recent times, Human activity recognition (HAR) becomes a major challenging issue among computer vision applications in day to day lives. The HAR is mainly envisioned to be utilized with other technologies, namely Internet of Things (IoT) and sensor technologies. Due to the advancements of deep learning (DL) approaches, automated high level feature extraction process can be utilized to improve the HAR results. In addition, DL techniques can be employed in different domains of sensor enabled HAR. In this aspect, this study designs an optimal DL based HAR (ODL-HAR) model on sensor enabled IoT environments. The ODL-HAR technique aims to determine human activities in day to day lives using wearables and IoT devices. The proposed ODL-HAR technique involves different stages of operations namely data acquisition, data preprocessing, feature extraction, classification, and parameter optimization. The ODL-HAR technique uses a MobileNet-v2 model as a feature extractor and bidirectional long short-term memory (BiLSTM) model as a classifier. In order to optimally tune the hyperparameter involved in the BiLSTM model, chaos game optimization (CGO) algorithm is employed and thereby raises the recognition performance. The design of CGO algorithm for hyperparameter optimization of HAR shows the novelty of the work. A wide range of simulations takes place to point out the betterment of the ODL-HAR technique on two benchmark datasets. The experimental results portrayed the enhanced performance of the ODL-HAR technique over the other recent HAR approaches interms of different evaluation parameters.

show abstract

Section: Related Workmentioning

confidence: 99%

Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

Al‐Wesabi¹,

Albraikan

Hilal

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The computer vision technology completes the recognition and classification of images. The computer is also used to analyze and understand the image content, simulate the thinking mode of human, and automatically extract the image features [5][6][7]. At present, deep learning performs well in visual recognition, speech recognition, image recognition, and other aspects.…”

Section: Introductionmentioning

confidence: 99%

“…YOLO [5,11] proposed by Redmon is an earlier end-toend detection method. The input image is first divided into s × s grid cells, and then, the direct input is resized to the convolution neural network structure that consisted of 24 convolutions with two full connection layers.…”

Section: Introductionmentioning

confidence: 99%

Multideep Feature Fusion Algorithm for Clothing Style Recognition

Wang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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.

show abstract

“…Understanding action in images and videos is of growing interest for the researchers in the field of artificial intelligence and image understanding related fields. It plays a significant role in real world applications such as activity monitoring, interactions between computers and humans, and video indexing [1][2][3] etc. The problem remains an ongoing challenge due to factors like uneven illumination effects, partial occlusion, and complex background [4].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed hybrid deep learning models for classification of action images are presented in Sect. 3. Section 4 provides variety of experiments to validate the proposed method which includes ablation study, experiments on classification of action images and experiments on text detection and recognition.…”

Section: Introductionmentioning

confidence: 99%

A deep action-oriented video image classification system for text detection and recognition

et al. 2021

View full text Add to dashboard Cite

For the video images with complex actions, achieving accurate text detection and recognition results is very challenging. This paper presents a hybrid model for classification of action-oriented video images which reduces the complexity of the problem to improve text detection and recognition performance. Here, we consider the following five categories of genres, namely concert, cooking, craft, teleshopping and yoga. For classifying action-oriented video images, we explore ResNet50 for learning the general pixel-distribution level information and the VGG16 network is implemented for learning the features of Maximally Stable Extremal Regions and again another VGG16 is used for learning facial components obtained by a multitask cascaded convolutional network. The approach integrates the outputs of the three above-mentioned models using a fully connected neural network for classification of five action-oriented image classes. We demonstrated the efficacy of the proposed method by testing on our dataset and two other standard datasets, namely, Scene Text Dataset dataset which contains 10 classes of scene images with text information, and the Stanford 40 Actions dataset which contains 40 action classes without text information. Our method outperforms the related existing work and enhances the class-specific performance of text detection and recognition, significantly. Article highlights The method uses pixel, stable-region and face-component information in a noble way for solving complex classification problems. The proposed work fuses different deep learning models for successful classification of action-oriented images. Experiments on our own dataset as well as standard datasets show that the proposed model outperforms related state-of-the-art (SOTA) methods.

show abstract

A resource conscious human action recognition framework using 26-layered deep convolutional neural network

Cited by 72 publications

References 41 publications

Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

Multideep Feature Fusion Algorithm for Clothing Style Recognition

A deep action-oriented video image classification system for text detection and recognition

Contact Info

Product

Resources

About