Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors

Sun, Jian; Fu, Yongling; Li, Shengguang; He, Jie; Xu, Chunjing; Tan, Lin

doi:10.1155/2018/8580959

Cited by 63 publications

(42 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the hybrid methods, it should highlight the hybrid CNN-LSTM-ELM system, invented by a group of Chinese scientists (Sun, 2018), which perfectly recognizes human activity (HAR). Figure 2 shows a visual diagram of this process.…”

Section: Methods and Datasets For Face And Eye Detection And Recognitionmentioning

confidence: 99%

Eye Recognition System to Prevent Accidents on the Road

Andriyanov

Lutfullina

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Today, possibilities of artificial intelligence allow us to see the emergence of autonomous cars. However, there are still many problems in this area at present. Often, such vehicles are “too slow to think”, are not able to reliably process data from video cameras in the event of reflections, glare, and there are also questions about the safety of such driving in difficult weather conditions or in heavy traffic. At the same time, the human factor plays a major role in accidents of driven vehicles. Many accidents involve driver fatigue, distraction, or even falling asleep. At the same time, it is potentially possible to monitor the state of a person behind the wheel by a video sequence received from a camera installed in the car's interior and registering the driver's face in video sequence. In this paper, the existing databases of images of faces and eyes are considered, and an algorithm is presented that detects the state of closed eyes based on Haar detectors and convolutional neural networks.

show abstract

Section: Methods and Datasets For Face And Eye Detection And Recognitionmentioning

confidence: 99%

Eye Recognition System to Prevent Accidents on the Road

Andriyanov

Lutfullina

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…There are many face classification techniques in the literature that allow to select, from a few examples, the group or class to which the objects belong. Some of them are based on statistics, such as the Bayesian classifier and correlation [18], and so on, and others based on the regions that generate the different classes in the decision space, such as K-means [9], CNN [103], artificial neural networks (ANNs) [37], support vector machines (SVMs) [26,107], k-nearest neighbors (K-NNs), decision trees (DTs), and so on.…”

Section: Classifiersmentioning

confidence: 99%

“…The ORL database is used for evaluation. [103] propose a hybrid deep structure called CNN-LSTM-ELM in order to achieve sequential human activity recognition (HAR). Their proposed CNN-LSTM-ELM structure is evaluated using the OPPORTUNITY dataset, which contains 46,495 training samples and 9894 testing samples, and each sample is a sequence.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

334

121

View full text Add to dashboard Cite

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

show abstract

“…Due to no requirement of domain knowledge in feature extraction, the authors in [6] proposed a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier.…”

Section: Convolutional Network and Extreme Learning Machine Classifiermentioning

confidence: 99%

Long Short Term Memory network for Recognition of Daily Human Activity

Arora¹,

Ahlawat²,

Payal³

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

View full text Add to dashboard Cite

Previously Human Activity Recognition has been solved by using the engineered features, but the main problem with this approach is that it requires specific domain knowledge. Adopting classical machine learning models has been effective but these methods completely ignore the time signal obtained from the sensors. But due to recent advancements, deep learning techniques like Long Short Term Model and recurrent neural networks have been effectively used to provide good results and classify human activities correctly as compared to machine learning models. So in this paper, we propose a LSTM framework to classify these activities and also compare its performance with classical machine learning models.

show abstract

Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors

Cited by 63 publications

References 28 publications

Eye Recognition System to Prevent Accidents on the Road

Eye Recognition System to Prevent Accidents on the Road

Face Recognition Systems: A Survey

Long Short Term Memory network for Recognition of Daily Human Activity

Contact Info

Product

Resources

About