Optimized hybrid RNN model for human activity recognition in untrimmed video

Deotale, Disha; Verma, Madhushi; Suresh, P.; Kotecha, Ketan

doi:10.1117/1.jei.31.5.051409

Cited by 4 publications

(4 citation statements)

References 40 publications

(59 reference statements)

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“…In [ 33 ], the authors proposed a new deep learning method using a hybrid gated recurrent unit (GRU) and LSTM -based RNN model for HAR. They used the TRECVID dataset to test the performance of the proposed deep learning model, which showed significant performance.…”

Section: Related Workmentioning

confidence: 99%

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

et al. 2022

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In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications. The applied MH were the Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA), artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the motion patterns and reduce the time complexities. Second, we developed a light feature extraction technique using advanced deep learning approaches. The developed model was ResRNN and was composed of several building blocks from deep learning networks including convolution neural networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we applied the mentioned MH algorithms to select the optimal features and boost classification accuracy. Finally, the support vector machine and random forest classifiers were employed to classify each activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary classification. We used seven different and complex datasets for the multi-classification case: the PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the results of the nine MH optimization methods using different performance indicators. We concluded that MH optimization algorithms had promising performance in HAR and fall detection applications.

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Section: Related Workmentioning

confidence: 99%

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

et al. 2022

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“…More emphasis has been given to the neural network in designing such a system. Results of the neural network techniques 1 are quite impressive compared to the traditional way. This paper explores our approach, which is unique because it provides a comprehensive study of image denoising using the handcrafted technique and neural network-based technique.…”

Section: Introductionmentioning

confidence: 99%

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Roka

Diwakar

2023

J. Electron. Imag.

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.Due to the increase of crime and terror, security concerns are rising rapidly every day. The use of surveillance cameras for abnormal behavior detection has become an indispensable part of human beings. But the performance of most of the developed systems is not up to the mark because of the low performance and accuracy in detecting the abnormality in the videos due to mainly the presence of noise. The videos captured by the surveillance camera are generally born with no or more noise due to various reasons. To resolve such issues, we provide a snapshot regarding different categories of noise and handcraft techniques to resolve them. Non-local means, block matching, and 3D filtering filters perform astonishingly well while denoising the images. We also present a robust unsupervised deep learning model called deep stacked denoising autoencoder (DSDAE) for denoising the images and further use it for abnormal activity detection and localization in the videos. Our approach has achieved a noteworthy result in image denoising compared to other handcraft-based techniques. DSDAE uses a separate encoder for the extraction of appearance features using clean and noisy images and motion features through the optical flow images. Early fusion is done in the extracted features and passed to the decoder. Only those pixels whose reconstruction error is greater than the threshold will be considered abnormal pixels. Experiment results are compared quantitatively/qualitatively with the recent competitive state-of-the-art methods in the publicly available benchmark datasets Ped1, Ped2, CUHK Avenue, and ShanghaiTech that demonstrate the superior accuracy and performance of our DSDAE. The obtained area under the curve of DSDAE in Ped1, Ped2, CUHK Avenue, and ShanghaiTech is 98.14%, 97.92%, 95.89%, and 96.7%, respectively, whereas equal error rate for the same datasets is 5.4%, 4.5%, 12.03%, and 7.8%, respectively.

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“…However, All the machine learning algorithms require manual labeling of discrete actions into letters, demanding significant time and effort. Some researchers automatically extracted features from image and recognized human activities [9][10][11][12][13]. Karpathy A proposed the LTC-CNN algorithm, a full convolutional network that achieved recognition accuracy of 63.3% on the UCF101 dataset [14].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 7. The structure of positional encoder model 3.4 Multi-layer Perceptron (MLP) classifierThe multi-layer neural network is represented by Equation(10), where W1 and W2 denote the weights of the first and second layers respectively, while b1 and b2 represent the biases of the first and…”

mentioning

confidence: 99%

HAR-ViT:A human activity recognition method based on ViT

Han,

Zeng,

Kuang

et al. 2024

Preprint

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Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality, and human-computer intelligent interaction. It has emerged as a significant research area in computer vision. Key algorithms utilized include P-LSTM (partial sensing LSTM), ST-GCN (Spatiotemporal graph convolutional networks), and 2s-AGCN (two-flow adaptive graph convolutional networks). Despite the remarkable achievements made by these algorithms, there are still some challenges to address, including unsatisfactory recognition accuracy, convergence difficulties, and limited generalization ability. To tackle these issues, this paper proposes HAR-ViT - a human activity recognition method based on Vision Transformer architecture. The enhanced AGCN (eAGCN) map filter is employed to assign weights to human skeleton data, highlighting key nodes and promoting model convergence. The position encoder module captures precise timing information while the transformer encoder efficiently compresses sequence data features to enhance calculation speed. Human activity recognition is accomplished through multi-layer perceptron (MLP) classifiers. Experimental results demonstrate that the proposed method achieves an accuracy of 91.06% for cross-subject and 96.73% for cross-view human behavior recognition on the NTU60 dataset respectively; similarly achieving accuracies of 87.61% and 89.02% on the NTU120 dataset for the same task respectively compared to state-of-the-art algorithms with an improved accuracy of approximately 1%, while reducing total parameter count by 57.24%.

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Optimized hybrid RNN model for human activity recognition in untrimmed video

Cited by 4 publications

References 40 publications

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

HAR-ViT:A human activity recognition method based on ViT

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