Human Activity Recognition and Location Based on Temporal Analysis

Ding, Hongjin; Gong, Fengkui; Gong, Wenjuan; Yuan, Xiao Ming; Ma, Yuhui

doi:10.1155/2018/4752191

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…This accuracy can be improved further by applying fusion models, as proposed in Refs. 40 and 41, in which neural architecture search and temporal analysis are employed to increase the HAR process’s feature extraction and selection capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized hybrid RNN model for human activity recognition in untrimmed video

Deotale¹,

Verma

Suresh

et al. 2022

J. Electron. Imag.

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. Human activity recognition is a field of video processing that requires restricted temporal analysis of video sequences for estimating the existence of different human actions. Designing an efficient human activity model requires credible implementations of keyframe extraction, preprocessing, feature extraction and selection, classification, and pattern recognition methods. In the real-time video, sequences are untrimmed and do not have any activity endpoints for effective recognition. Thus, we propose a hybrid gated recurrent unit and long short-term memory-based recurrent neural network model for high-efficiency human action recognition in untrimmed video datasets. The proposed model is tested on the TRECVID dataset, along with other online datasets, and is observed to have an accuracy of over 91% for untrimmed video-based activity recognition. This accuracy is compared with various state-of-the-art models and is found to be higher when evaluated on multiple datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Optimized hybrid RNN model for human activity recognition in untrimmed video

Deotale¹,

Verma

Suresh

et al. 2022

J. Electron. Imag.

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show abstract

“…Standing, walking, and falling were all recognized and located in the uncut long video. In [7], an automatic human activity identification system that recognizes human's actions without human intervention has been described. The authors tested four deep learning approaches and thirteen machine learning algorithms, including neural networks, random forest, support vector machine (SVM), decision tree classifier, and others, to find the most efficient process of human movement recognition.…”

Section: Related Workmentioning

confidence: 99%

HARTIV: Human Activity Recognition Using Temporal Information in Videos

Deotale¹,

Verma²,

Suresh³

et al. 2022

Computers, Materials &Amp; Continua

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Nowadays, the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data. The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos. Smarter monitoring is a historical necessity in which commonly occurring, regular, and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology. In a long video, human activity may be present anywhere in the video. There can be a single or multiple human activities present in such videos. This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment. The recognition process is split into four parts: firstly, the video is divided into different set of frames, then the human body part in a sequence of frames is identified, next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm. The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS. Three sports activities like swimming, cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e., the start and end time for every activity present in the video. The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity. The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.

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