Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Zhang, Rui

doi:10.1155/2022/6912315

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…There have been several efforts to apply swarm intelligence to action recognition from video. One such approach employs a combination of binary histogram, Harris corner points, and wavelet coefficients as features extracted from the spatiotemporal volume of the video sequence 42 . To minimize computational complexity, the feature space is reduced through the use of PSO with a multi-objective fitness function.…”

Section: Related Workmentioning

confidence: 99%

Video action recognition collaborative learning with dynamics via PSO-ConvNet Transformer

Nguyen,

Ribeiro

2023

Sci Rep

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Recognizing human actions in video sequences, known as Human Action Recognition (HAR), is a challenging task in pattern recognition. While Convolutional Neural Networks (ConvNets) have shown remarkable success in image recognition, they are not always directly applicable to HAR, as temporal features are critical for accurate classification. In this paper, we propose a novel dynamic PSO-ConvNet model for learning actions in videos, building on our recent work in image recognition. Our approach leverages a framework where the weight vector of each neural network represents the position of a particle in phase space, and particles share their current weight vectors and gradient estimates of the Loss function. To extend our approach to video, we integrate ConvNets with state-of-the-art temporal methods such as Transformer and Recurrent Neural Networks. Our experimental results on the UCF-101 dataset demonstrate substantial improvements of up to 9% in accuracy, which confirms the effectiveness of our proposed method. In addition, we conducted experiments on larger and more variety of datasets including Kinetics-400 and HMDB-51 and obtained preference for Collaborative Learning in comparison with Non-Collaborative Learning (Individual Learning). Overall, our dynamic PSO-ConvNet model provides a promising direction for improving HAR by better capturing the spatio-temporal dynamics of human actions in videos. The code is available at https://github.com/leonlha/Video-Action-Recognition-Collaborative-Learning-with-Dynamics-via-PSO-ConvNet-Transformer.

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Section: Related Workmentioning

confidence: 99%

Video action recognition collaborative learning with dynamics via PSO-ConvNet Transformer

Nguyen,

Ribeiro

2023

Sci Rep

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“…It is worth mentioning that MH optimization algorithms including SI methods have limited applications in HAR systems. Almost all of the MH and SI method applications were in computer vision-based HAR applications such as the PSO [ 47 , 48 ] and genetic algorithm (GA) [ 49 ]. For sensor data, we carried out a simple implementation for the arithmetic optimization algorithm (AOA) with the KU-HAR, UCI-HAR, and WISDM datasets from [ 11 ] and the grey wolf optimizer using WISDM and UCI-HAR [ 50 ].…”

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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“…Particle Swarm Optimization in movement detection is based on the concept of variation and inter-frame difference for feature selection. The swarm algorithms are mainly used in human motion detection in sports, and it is used based on probabilistic optimization algorithm [43]- [46] and CNN [47].…”

mentioning

confidence: 99%

Preference Neural Network

Prasad¹,

Lin²,

Elgharabawy³

2023

Preprint

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Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

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Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Cited by 7 publications

References 27 publications

Video action recognition collaborative learning with dynamics via PSO-ConvNet Transformer

Video action recognition collaborative learning with dynamics via PSO-ConvNet Transformer

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

Preference Neural Network

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