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Journal articles on the topic 'Spatiotemporal feature extraction'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Sun, Weitong, Xingya Yan, Yuping Su, Gaihua Wang, and Yumei Zhang. "MSDSANet: Multimodal Emotion Recognition Based on Multi-Stream Network and Dual-Scale Attention Network Feature Representation." Sensors 25, no. 7 (2025): 2029. https://doi.org/10.3390/s25072029.

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Aiming at the shortcomings of EEG emotion recognition models in feature representation granularity and spatiotemporal dependence modeling, a multimodal emotion recognition model integrating multi-scale feature representation and attention mechanism is proposed. The model consists of a feature extraction module, feature fusion module, and classification module. The feature extraction module includes a multi-stream network module for extracting shallow EEG features and a dual-scale attention module for extracting shallow EOG features. The multi-scale and multi-granularity feature fusion improves

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Hoffmann, Susanne, Alexander Warmbold, Lutz Wiegrebe, and Uwe Firzlaff. "Spatiotemporal contrast enhancement and feature extraction in the bat auditory midbrain and cortex." Journal of Neurophysiology 110, no. 6 (2013): 1257–68. http://dx.doi.org/10.1152/jn.00226.2013.

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Navigating on the wing in complete darkness is a challenging task for echolocating bats. It requires the detailed analysis of spatial and temporal information gained through echolocation. Thus neural encoding of spatiotemporal echo information is a major function in the bat auditory system. In this study we presented echoes in virtual acoustic space and used a reverse-correlation technique to investigate the spatiotemporal response characteristics of units in the inferior colliculus (IC) and the auditory cortex (AC) of the bat Phyllostomus discolor. Spatiotemporal response maps (STRMs) of IC u

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Mehrez, Ahmed, Ahmed A. Morgan, and Elsayed E. Hemayed. "Speeding up spatiotemporal feature extraction using GPU." Journal of Real-Time Image Processing 16, no. 6 (2018): 2379–407. http://dx.doi.org/10.1007/s11554-018-0755-2.

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Kamarol, Siti Khairuni Amalina, Jussi Parkkinen, Mohamed Hisham Jaward, and Rajendran Parthiban. "Spatiotemporal feature extraction for facial expression recognition." IET Image Processing 10, no. 7 (2016): 534–41. http://dx.doi.org/10.1049/iet-ipr.2015.0519.

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Al-Shakarchy, Noor D., and Israa Hadi Ali. "Drowsy Detection based on Spatiotemporal Feature Extraction of Video Using 3D-CNN." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 742–51. http://dx.doi.org/10.5373/jardcs/v11sp10/20192865.

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Al-Shakarchy, Noor D. "Drowsy Detection based on Spatiotemporal Feature Extraction of Video Using 3D-CNN." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 742–51. http://dx.doi.org/10.5373/jardcs/v11sp10/201928650.

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Wang, Rui. "Human Activity Recognition Algorithm Based on Bidirectional Multi-Channel Feature Fusion." Applied and Computational Engineering 146, no. 1 (2025): 9–14. https://doi.org/10.54254/2755-2721/2025.21590.

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This paper designs a bidirectional spatiotemporal feature fusion algorithm for human activity recognition based on frequency modulated continuous wave radar. The algorithm takes the three-dimensional point cloud data of human activity collected by the radar as input, and adopts a dual channel feature extraction method in spatial feature extraction. The voxelated point cloud data is put into a convolutional neural network for extracting coarse-grained spatial information. At the same time, a multi-layer perceptron is used to extract fine-grained spatial information from individual points in the

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Zhang, Bowen, and Tianqi Wang. "Visual Image Recognition of Basketball Turning and Dribbling Based on Feature Extraction." Traitement du Signal 39, no. 6 (2022): 2115–21. http://dx.doi.org/10.18280/ts.390624.

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The processing of basketball videos with complex contents faces several challenges in terms of global motion features, group motion features, and individual pose features. The current research cannot solve problems, such as the diverse spatiotemporal features of actions, the utilization of correspondence between spatiotemporal features, the increase of data volume, and the complexity of the network. To solve these problems, this paper studies the visual image recognition of basketball turning and dribbling based on feature extraction. Specifically, the optical flow image was introduced to esta

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Young, S. R., A. Davis, A. Mishtal, and I. Arel. "Hierarchical spatiotemporal feature extraction using recurrent online clustering." Pattern Recognition Letters 37 (February 2014): 115–23. http://dx.doi.org/10.1016/j.patrec.2013.07.013.

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Lu, Shuang, Qian Zhang, Yi Liu, Lei Liu, Qing Zhu, and Ke Jing. "Retrieval of Multiple Spatiotemporally Correlated Images on Tourist Attractions Based on Image Processing." Traitement du Signal 37, no. 5 (2020): 847–54. http://dx.doi.org/10.18280/ts.370518.

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The thriving of information technology (IT) has elevated the demand for intelligent query and retrieval of information about the tourist attractions of interest, which are the bases for preparing convenient and personalized itineraries. To realize accurate and rapid query of tourist attraction information (not limited to text information), this paper proposes a spatiotemporal feature extraction method and a ranking and retrieval method for multiple spatiotemporally correlated images (MSCIs) on tourist attractions based on deeply recursive convolutional network (DRCN). Firstly, the authors intr

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Liu, Lu, Yibo Cao, and Yuhan Dong. "Attention-Based Multiple Graph Convolutional Recurrent Network for Traffic Forecasting." Sustainability 15, no. 6 (2023): 4697. http://dx.doi.org/10.3390/su15064697.

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Traffic forecasting is essential in the development of intelligent transportation systems, as it enables the formulation of effective traffic dispatching strategies and contributes to the reduction of traffic congestion. The abundance of research focused on modeling complex spatiotemporal correlations for accurate traffic prediction, however many of these prior works perform feature extraction based solely on prior graph structures, thereby overlooking the latent graph connectivity inherent in the data and degrading a decline in prediction accuracy. In this study, we present a novel Attention-

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Shi, Junren, Jun Gao, and Sheng Xiang. "Adaptively Lightweight Spatiotemporal Information-Extraction-Operator-Based DL Method for Aero-Engine RUL Prediction." Sensors 23, no. 13 (2023): 6163. http://dx.doi.org/10.3390/s23136163.

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Accurate prediction of machine RUL plays a crucial role in reducing human casualties and economic losses, which is of significance. The ability to handle spatiotemporal information contributes to improving the prediction performance of machine RUL. However, most existing models for spatiotemporal information processing are not only complex in structure but also lack adaptive feature extraction capabilities. Therefore, a lightweight operator with adaptive spatiotemporal information extraction ability named Involution GRU (Inv-GRU) is proposed for aero-engine RUL prediction. Involution, the adap

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Li, Wenfeng, and Xiao Zhou. "Precipitation nowcasting method based on Spatial-Temporal Dual Discriminators." Journal of Physics: Conference Series 2816, no. 1 (2024): 012038. http://dx.doi.org/10.1088/1742-6596/2816/1/012038.

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Abstract The prediction of radar echo sequence images is a spatiotemporal sequence forecasting problem, which is one of the main challenges in precipitation nowcasting. Addressing issues such as poor extraction of spatiotemporal features by previous models and blurry image sequence predictions, this study proposes a Spatial-Temporal dual Discriminator Precipitation Nowcasting Model (STD-SNGAN) based on spectral normalization generative adversarial networks (SNGAN). The model utilizes multi-scale convolution modules (Inception) to extract spatial features from radar echo images and convolutiona

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Ma, Yahong, Zhentao Huang, Yuyao Yang, et al. "MSBiLSTM-Attention: EEG Emotion Recognition Model Based on Spatiotemporal Feature Fusion." Biomimetics 10, no. 3 (2025): 178. https://doi.org/10.3390/biomimetics10030178.

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Emotional states play a crucial role in shaping decision-making and social interactions, with sentiment analysis becoming an essential technology in human–computer emotional engagement, garnering increasing interest in artificial intelligence research. In EEG-based emotion analysis, the main challenges are feature extraction and classifier design, making the extraction of spatiotemporal information from EEG signals vital for effective emotion classification. Current methods largely depend on machine learning with manual feature extraction, while deep learning offers the advantage of automatic

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Li, Weisheng, Fengyan Wu, and Dongwen Cao. "Dual-Branch Remote Sensing Spatiotemporal Fusion Network Based on Selection Kernel Mechanism." Remote Sensing 14, no. 17 (2022): 4282. http://dx.doi.org/10.3390/rs14174282.

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Popular deep-learning-based spatiotemporal fusion methods for creating high-temporal–high-spatial-resolution images have certain limitations. The reconstructed images suffer from insufficient retention of high-frequency information and the model suffers from poor robustness, owing to the lack of training datasets. We propose a dual-branch remote sensing spatiotemporal fusion network based on a selection kernel mechanism. The network model comprises a super-resolution network module, a high-frequency feature extraction module, and a difference reconstruction module. Convolution kernel adaptive

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Geng, Liangchao, Huantong Geng, Jinzhong Min, Xiaoran Zhuang, and Yu Zheng. "AF-SRNet: Quantitative Precipitation Forecasting Model Based on Attention Fusion Mechanism and Residual Spatiotemporal Feature Extraction." Remote Sensing 14, no. 20 (2022): 5106. http://dx.doi.org/10.3390/rs14205106.

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Reliable quantitative precipitation forecasting is essential to society. At present, quantitative precipitation forecasting based on weather radar represents an urgently needed, yet rather challenging. However, because the Z-R relation between radar and rainfall has several parameters in different areas, and because rainfall varies with seasons, traditional methods cannot capture high-resolution spatiotemporal features. Therefore, we propose an attention fusion spatiotemporal residual network (AF-SRNet) to forecast rainfall precisely for the weak continuity of convective precipitation. Specifi

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Li, Tiankuo, Hongji Xu, Zhi Liu, et al. "A spatiotemporal multi-feature extraction framework for opinion mining." Neurocomputing 490 (June 2022): 337–46. http://dx.doi.org/10.1016/j.neucom.2021.11.098.

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Bao, Nengsheng, Yawei Ma, Xiang Wei, and Zuodong Liang. "Dynamic facial expression recognition based on attention mechanism." Journal of Physics: Conference Series 2816, no. 1 (2024): 012108. http://dx.doi.org/10.1088/1742-6596/2816/1/012108.

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Abstract In the wild, dynamic facial emotion recognition is a highly challenging task. Traditional approaches often focus on extracting discriminative features or preprocessing data to remove noisy frames. The former overlooks differences between keyframes and noise frames, while the latter can be complex and less robust. To address this issue, we propose a spatiotemporal feature extraction network based on an attention mechanism. In the spatial feature extraction stage, our method incorporates prior knowledge through an attention mechanism, allowing the model to precisely select and focus on

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P, Geethanjali. "Hybrid Features-Based Intrusion Detection for The Internet of Vehicles using Dynamic Adaptation." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 2308–16. http://dx.doi.org/10.22214/ijraset.2023.57839.

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Abstract: The evolving landscape of the Internet of Vehicles (IoV) has brought to the forefront a discernible array of challenges about network security. In response, this study delves into applying deep learning-based intrusion detection techniques to fortify the IoV against potential network threats. Notably, prevailing approaches often rely on a singular deep learning model for either temporal or spatial feature extraction, with a serial sequence of spatial feature extraction followed by temporal feature extraction. Such methodologies tend to exhibit shortcomings in adequately capturing the

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Naeem, Shehryar, Hanan Salam, and Md Azher Uddin. "Pakistani Word-level Sign Language Recognition Based on Deep Spatiotemporal Network." Proceedings of the AAAI Symposium Series 6, no. 1 (2025): 119–26. https://doi.org/10.1609/aaaiss.v6i1.36042.

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Sign language is crucial for the Deaf and Hard-of-Hearing community because it facilitates visual movement-based communication. Nevertheless, most are not familiar with it, rendering interactions with the hearing impaired complicated. While there has been significant work on languages, for instance, American and Chinese Sign Language, Pakistani Sign Language (PSL) at the word level has received less attention and has been studied based on static images. To address this, we introduce a deep spatiotemporal network for word-level PSL recognition from video. It commences by employing top-k frame e

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Jiang, Shan, Yuming Feng, Xiaofeng Liao, Hongjuan Wu, Jinkui Liu, and Babatunde Oluwaseun Onasanya. "A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic Speed." Symmetry 16, no. 5 (2024): 537. http://dx.doi.org/10.3390/sym16050537.

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Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spati

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Wang, Hui. "Research on Video Emotion Recognition Method Based on Deep Learning." International Journal of Computer Science and Information Technology 5, no. 2 (2025): 95–103. https://doi.org/10.62051/ijcsit.v5n2.12.

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Video data has significant temporal and spatial characteristics, and the extraction and recognition of its emotional features is currently a research hotspot in the field of human-computer interaction. This paper proposes an improved model based on P3D 3D residual network (3Res att Network) to address the difficulties in dynamic feature extraction and insufficient spatiotemporal information fusion in video emotion recognition tasks. Firstly, the P3D infrastructure is constructed by decoupling spatiotemporal convolution kernels, effectively reducing model complexity; Secondly, design a 3D Spati

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Qi, Xueying, Weijian Hu, Baoshan Li, and Ke Han. "STGNN-FAM: A Traffic Flow Prediction Model for Spatiotemporal Graph Networks Based on Fusion of Attention Mechanisms." Journal of Advanced Transportation 2023 (May 24, 2023): 1–19. http://dx.doi.org/10.1155/2023/8880530.

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Network traffic state prediction has been constantly challenged by complex spatiotemporal features of traffic information as well as imperfection in streaming data. This paper proposes a traffic flow prediction model for spatiotemporal graph networks based on fusion of attention mechanisms (STGNN-FAM) to simultaneously tackle these challenges. This model contains a spatial feature extraction layer, a bidirectional temporal feature extraction layer, and an attention fusion layer, which not only fully considers the temporal and spatial features of the traffic flow problem but also uses the atten

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Sadek, Samy, Ayoub Al-Hamadi, Gerald Krell, and Bernd Michaelis. "Affine-Invariant Feature Extraction for Activity Recognition." ISRN Machine Vision 2013 (July 15, 2013): 1–7. http://dx.doi.org/10.1155/2013/215195.

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We propose an innovative approach for human activity recognition based on affine-invariant shape representation and SVM-based feature classification. In this approach, a compact computationally efficient affine-invariant representation of action shapes is developed by using affine moment invariants. Dynamic affine invariants are derived from the 3D spatiotemporal action volume and the average image created from the 3D volume and classified by an SVM classifier. On two standard benchmark action datasets (KTH and Weizmann datasets), the approach yields promising results that compare favorably wi

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Xu, Chenrui, Zhenfei Wang, Liang Chen, and Xiangchao Meng. "Spatiotemporal Interactive Learning for Cloud Removal Based on Multi-Temporal SAR–Optical Images." Remote Sensing 17, no. 13 (2025): 2169. https://doi.org/10.3390/rs17132169.

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Optical remote sensing images suffer from information loss due to cloud interference, while Synthetic Aperture Radar (SAR), capable of all-weather and day–night imaging capabilities, provides crucial auxiliary data for cloud removal and reconstruction. However, existing cloud removal methods face the following key challenges: insufficient utilization of spatiotemporal information in multi-temporal data, and fusion challenges arising from fundamentally different imaging mechanisms between optical and SAR images. To address these challenges, a spatiotemporal feature interaction-based cloud remov

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Fan, Chaoyu. "Human Behavior Recognition Based on CNN-LSTM Hybrid and Multi-Sensing Feature Information Fusion." Journal of Combinatorial Mathematics and Combinatorial Computing 118 (December 31, 2023): 143–54. http://dx.doi.org/10.61091/jcmcc118-11.

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To address the human activity recognition problem and its application in practical situations, a CNN-LSTM hybrid neural network model capable of automatically extracting sensor data features and memorizing temporal activity data is designed and improved by integrating CNN and gated recurrent units as a variant of RNN. A multi-channel spatiotemporal fusion network-based two-person interaction behavior recognition method is proposed for two-person skeletal sequential behavior recognition. Firstly, a viewpoint invariant feature extraction method is used to extract two-player skeleton features, th

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Lei, Dajiang, Gangsheng Ran, Liping Zhang, and Weisheng Li. "A Spatiotemporal Fusion Method Based on Multiscale Feature Extraction and Spatial Channel Attention Mechanism." Remote Sensing 14, no. 3 (2022): 461. http://dx.doi.org/10.3390/rs14030461.

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Remote sensing satellite images with a high spatial and temporal resolution play a crucial role in Earth science applications. However, due to technology and cost constraints, it is difficult for a single satellite to achieve both a high spatial resolution and high temporal resolution. The spatiotemporal fusion method is a cost-effective solution for generating a dense temporal data resolution with a high spatial resolution. In recent years, spatiotemporal image fusion based on deep learning has received wide attention. In this article, a spatiotemporal fusion method based on multiscale featur

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Long, Wei, Jintao Zhang, Linhua Jiang, Yuanyuan Yang, Yuwei Tang, and Lingxi Hu. "Fish Feeding Behavior Recognition Based on Enhanced MobileViTv3 Model." Journal of Computing and Electronic Information Management 16, no. 2 (2025): 11–20. https://doi.org/10.54097/cacjn071.

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Video stream-based fish feeding behavior recognition has garnered significant attention in recent years, accelerating the optimization of feeding strategies and enhancing aquaculture efficiency. However, current feeding intensity assessment methods suffer from inefficiency and subjectivity in manual observation, compounded by challenges in accurately extracting behavioral features due to high mobility and random movement patterns of outdoor-cultured fish. Constructing an efficient multi-feature extraction model for fish feeding recognition—particularly deployable on mobile and edge devices—rem

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Cheng, Feifei, Zhitao Fu, Bohui Tang, Liang Huang, Kun Huang, and Xinran Ji. "STF-EGFA: A Remote Sensing Spatiotemporal Fusion Network with Edge-Guided Feature Attention." Remote Sensing 14, no. 13 (2022): 3057. http://dx.doi.org/10.3390/rs14133057.

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Spatiotemporal fusion in remote sensing plays an important role in Earth science applications by using information complementarity between different remote sensing data to improve image performance. However, several problems still exist, such as edge contour blurring and uneven pixels between the predicted image and the real ground image, in the extraction of salient features by convolutional neural networks (CNNs). We propose a spatiotemporal fusion method with edge-guided feature attention based on remote sensing, called STF-EGFA. First, an edge extraction module is used to maintain edge det

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Cheng, Hsu-Yung, Chih-Chang Yu, and Chenyu Li. "Separable ConvNet Spatiotemporal Mixer for Action Recognition." Electronics 13, no. 3 (2024): 496. http://dx.doi.org/10.3390/electronics13030496.

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Video action recognition is vital in the research area of computer vision. In this paper, we develop a novel model, named Separable ConvNet Spatiotemporal Mixer (SCSM). Our goal is to develop an efficient and lightweight action recognition backbone that can be applied to multi-task models to increase the accuracy and processing speed. The SCSM model uses a new hierarchical spatial compression, employing the spatiotemporal fusion method, consisting of a spatial domain and a temporal domain. The SCSM model maintains the independence of each frame in the spatial domain for feature extraction and

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Phapale, Anuja, and Sukhada Bhingarkar. "Deep Context-Aware Feature Extraction for Anomaly Detection in Surveillance Videos." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21633–38. https://doi.org/10.48084/etasr.9810.

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Surveillance video analysis plays a crucial role in ensuring public safety and security. Developing a context-aware framework for anomaly detection in surveillance videos is motivated by the need for enhanced security, safety, and efficiency in various domains. Context-aware anomaly detection depends on spatiotemporal features that help the model understand the context of anomalies in surveillance videos. This study aimed to provide a novel deep learning-based context-aware approach to feature extraction to detect anomalies in surveillance videos. The proposed method integrates ResNet50 for sp

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Tan, Xiaofeng, Xihai Li, Hongru Li, Xiaoniu Zeng, Shengjie Luo, and Tianyou Liu. "A Deep Learning Approach for Spatiotemporal Feature Classification of Infrasound Signals." Geosciences 15, no. 7 (2025): 251. https://doi.org/10.3390/geosciences15070251.

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Infrasound signal classification remains a critical challenge in geophysical monitoring systems, where classification performance is fundamentally constrained by feature extraction efficacy. Existing two-dimensional feature extraction methods suffer from inadequate representation of spatiotemporal signal dynamics, leading to performance degradation in long-distance detection scenarios. To overcome these limitations, we present a novel classification framework that effectively captures spatiotemporal infrasound characteristics through Gramian Angular Field (GAF) transformation. The proposed met

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Ma, Zhao, Shengliang Fang, Youchen Fan, Gaoxing Li, and Haojie Hu. "An Efficient and Lightweight Model for Automatic Modulation Classification: A Hybrid Feature Extraction Network Combined with Attention Mechanism." Electronics 12, no. 17 (2023): 3661. http://dx.doi.org/10.3390/electronics12173661.

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This paper proposes a hybrid feature extraction convolutional neural network combined with a channel attention mechanism (HFECNET-CA) for automatic modulation recognition (AMR). Firstly, we designed a hybrid feature extraction backbone network. Three different forms of convolution kernels were used to extract features from the original I/Q sequence on three branches, respectively, learn the spatiotemporal features of the original signal from different “perspectives” through the convolution kernels with different shapes, and perform channel fusion on the output feature maps of the three branche

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Tang, Ping, Ying Su, Weisheng Zhao, Qian Wang, Lianglin Zou, and Jifeng Song. "A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction." Energies 18, no. 12 (2025): 3193. https://doi.org/10.3390/en18123193.

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Accurate photovoltaic (PV) power forecasting is essential to mitigating the security and stability challenges associated with PV integration into power grids. Ground-based sky images can quickly reveal cloud changes, and the spatiotemporal feature information extracted from these images can improve PV power forecasting. Therefore, this paper proposes a hybrid framework based on shifted windows Transformer (Swin Transformer), convolutional neural network, and long short-term memory network to comprehensively extract spatiotemporal feature information, including global spatial, local spatial, an

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Zhang, Ya, Ravie Chandren Muniyandi, and Faizan Qamar. "A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance." Applied Sciences 15, no. 3 (2025): 1552. https://doi.org/10.3390/app15031552.

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In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. This article systematically reviews recent advancements in applying deep learning techniques in IDS, focusing on the core challenges of spatiotemporal feature extraction and data imbalance. First, this article analyzes the spatiotemporal dependencies of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in network traffic feature extracti

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Ji, Changpeng, Haofeng Yu, and Wei Dai. "Network Traffic Anomaly Detection Based on Spatiotemporal Feature Extraction and Channel Attention." Processes 12, no. 7 (2024): 1418. http://dx.doi.org/10.3390/pr12071418.

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To overcome the challenges of feature selection in traditional machine learning and enhance the accuracy of deep learning methods for anomaly traffic detection, we propose a novel method called DCGCANet. This model integrates dilated convolution, a GRU, and a Channel Attention Network, effectively combining dilated convolutional structures with GRUs to extract both temporal and spatial features for identifying anomalous patterns in network traffic. The one-dimensional dilated convolution (DC-1D) structure is designed to expand the receptive field, allowing for comprehensive traffic feature ext

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Lu, Wenjuan, Dongping Ming, Xi Mao, Jizhou Wang, Zhanjie Zhao, and Yao Cheng. "A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language." Applied Sciences 15, no. 3 (2025): 1073. https://doi.org/10.3390/app15031073.

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To address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Decoding-enhanced BERT with disentangled attention)-based natural language spatiotemporal question semantic conversion model. The model first performs semantic encoding on natural language spatiotemporal questions, extracts pre-trained features based on the DeBERTa model, inputs feature vector sequences into BiGRU (Bidirectional Gated Recurrent Unit)

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Chen, Zexiang. "Research Progress of Skeleton-Based Action Recognition Technologies." ITM Web of Conferences 73 (2025): 02022. https://doi.org/10.1051/itmconf/20257302022.

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Skeletal-based action recognition technology, which analyzes the spatiotemporal sequences of human skeletal joints to identify human behaviors, has garnered widespread attention in computer vision in recent years. This review aims to collate and summarize the research advancements in this domain, with a particular focus on the classification and comparison of feature extraction methodologies. The paper commences by elucidating the acquisition and preprocessing of skeletal data, laying the groundwork for subsequent feature extraction. The thematic focus of the research centers on two predominan

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Dai, Wensheng, Jui-Yu Wu, and Chi-Jie Lu. "Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/438132.

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Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this pape

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Wang, Rui, Jiahao Ren, Weidong Li, Teng Yu, Fan Zhang, and Jiangtao Wang. "Application of Instance Segmentation to Identifying Insect Concentrations in Data from an Entomological Radar." Remote Sensing 16, no. 17 (2024): 3330. http://dx.doi.org/10.3390/rs16173330.

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Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the dete

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Long, Yingying, Zongxin Wang, Hanzhu Wei, and Xiaojun Bai. "A Baseline for Violence Behavior Detection in Complex Surveillance Scenarios." International Journal of Advanced Network, Monitoring and Controls 9, no. 4 (2024): 48–58. https://doi.org/10.2478/ijanmc-2024-0036.

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Abstract Violence detection can improve the ability to deal with emergencies, but there is still no data set specifically for violence detection. In this work, we propose VioData, a datasets specialized for detection in complex surveillance scenarios, and to more accurately assess the efficacy of these datasets, we propose a violence detection model based on target detection and 3D convolution. The model consists of two key modules: spatio-temporal feature extraction module and spatiotemporal feature fusion module. Among them, the spatio-temporal feature extraction module consists of a spatial

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Hu, Kai, Yiwu Ding, Junlan Jin, Liguo Weng, and Min Xia. "Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features." Applied Sciences 12, no. 3 (2022): 1028. http://dx.doi.org/10.3390/app12031028.

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In the task of human motion recognition, the overall action span is changeable, and there may be an inclusion relationship between action semantics. This paper proposes a novel multi-scale time sampling module and a deep spatiotemporal feature extraction module, which strengthens the receptive field of the feature map and strengthens the extraction of spatiotemporal-related feature information via the network. We study and compare the performance of three existing multi-channel fusion methods to improve the recognition accuracy of the network on the open skeleton recognition dataset. In this p

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Han, Yanling, Xiaotong Wang, Haiyang He, Jing Wang, and Yun Zhang. "Habitat Prediction of Bigeye Tuna Based on Multi-Feature Fusion of Heterogenous Remote-Sensing Data." Journal of Marine Science and Engineering 12, no. 8 (2024): 1294. http://dx.doi.org/10.3390/jmse12081294.

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Accurate habitat prediction of Bigeye Tuna, the main fishing target of tuna pelagic fishery, is of great significance to the fishing operation. In response to the fact that most of the current studies use single-source data for habitat prediction, and the association between spatiotemporal features and habitat distribution is not fully explored and that this has limited the further improvement of prediction accuracy, this paper analyzes the spatiotemporal distribution of the characteristics of Bigeye Tuna’s highly migratory nature. Additionally, it puts forward a method of habitat prediction t

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Zhu, Yuan, Ruidong Xu, Chongben Tao, et al. "DS-Trans: A 3D Object Detection Method Based on a Deformable Spatiotemporal Transformer for Autonomous Vehicles." Remote Sensing 16, no. 9 (2024): 1621. http://dx.doi.org/10.3390/rs16091621.

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Facing the significant challenge of 3D object detection in complex weather conditions and road environments, existing algorithms based on single-frame point cloud data struggle to achieve desirable results. These methods typically focus on spatial relationships within a single frame, overlooking the semantic correlations and spatiotemporal continuity between consecutive frames. This leads to discontinuities and abrupt changes in the detection outcomes. To address this issue, this paper proposes a multi-frame 3D object detection algorithm based on a deformable spatiotemporal Transformer. Specif

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Zeng, Chunyan, Shixiong Feng, Dongliang Zhu, and Zhifeng Wang. "Source Acquisition Device Identification from Recorded Audio Based on Spatiotemporal Representation Learning with Multi-Attention Mechanisms." Entropy 25, no. 4 (2023): 626. http://dx.doi.org/10.3390/e25040626.

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Source acquisition device identification from recorded audio aims to identify the source recording device by analyzing the intrinsic characteristics of audio, which is a challenging problem in audio forensics. In this paper, we propose a spatiotemporal representation learning framework with multi-attention mechanisms to tackle this problem. In the deep feature extraction stage of recording devices, a two-branch network based on residual dense temporal convolution networks (RD-TCNs) and convolutional neural networks (CNNs) is constructed. The spatial probability distribution features of audio s

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Chen, Jinlong, Deming Luo, Zhigang Xiao, Minghao Yang, Xingguo Qin, and Yongsong Zhan. "Haptic–Vision Fusion for Accurate Position Identification in Robotic Multiple Peg-in-Hole Assembly." Electronics 14, no. 11 (2025): 2163. https://doi.org/10.3390/electronics14112163.

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Multi-peg-hole assembly is a fundamental process in robotic manufacturing, particularly for circular aviation electrical connectors (CAECs) that require precise axial alignment. However, CAEC assembly poses significant challenges due to small apertures, posture disturbances, and the need for high error tolerance. This paper proposes a dual-stream Siamese network (DSSN) framework that fuses visual and tactile modalities to achieve accurate position identification in six-degree-of-freedom robotic connector assembly tasks. The DSSN employs ConvNeXt for visual feature extraction and SE-ResNet-50 w

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Xie, Zaimi, Chunmei Mo, and Baozhu Jia. "A Novel Swin-Transformer with Multi-Source Information Fusion for Online Cross-Domain Bearing RUL Prediction." Journal of Marine Science and Engineering 13, no. 5 (2025): 842. https://doi.org/10.3390/jmse13050842.

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Accurate remaining useful life (RUL) prediction of rolling bearings plays a critical role in predictive maintenance. However, existing methods face challenges in extracting and fusing multi-source spatiotemporal features, addressing distribution differences between intra-domain and inter-domain features, and balancing global-local feature attention. To overcome these limitations, this paper proposes an online cross-domain RUL prediction method based on a swin-transformer with multi-source information fusion. The method uses a Bidirectional Long Short-Term Memory (Bi-LSTM) network to capture te

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Zhou, Jiaming, Ke Wei, Jiahuan Huang, Lin Yang, and Junzhe Shi. "Research on Water Quality Prediction Model Based on Spatiotemporal Weighted Fusion and Hierarchical Cross-Attention Mechanisms." Water 17, no. 9 (2025): 1244. https://doi.org/10.3390/w17091244.

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In the context of drinking water safety assurance, water quality prediction faces challenges due to temporal fluctuations, seasonal cycles, and the impacts of sudden events. To address the issue of cumulative prediction bias caused by the simplistic feature fusion of traditional methods, this study proposes a neural network architecture that integrates spatiotemporal features with a hierarchical cross-attention mechanism. Innovatively, the model constructs a parallel feature extraction framework, integrating BiGRUs (Bidirectional Gated Recurrent Units) and BiTCNs (Bidirectional Temporal Convol

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Li, Jie, Hui Wang, Jinliang Wang, Jianpeng Zhang, Yongcui Lan, and Yuncheng Deng. "Combining Multi-Source Data and Feature Optimization for Plastic-Covered Greenhouse Extraction and Mapping Using the Google Earth Engine: A Case in Central Yunnan Province, China." Remote Sensing 15, no. 13 (2023): 3287. http://dx.doi.org/10.3390/rs15133287.

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Rapidly increasing numbers of the plastic-covered greenhouse (PCG) worldwide ensure food security but threaten environmental security; thus, accurate monitoring of the spatiotemporal pattern in plastic-covered greenhouses (PCGs) is necessary for modern agricultural management and environmental protection. However, many urgent issues still exist in PCG mapping, such as multi-source data combination, classification accuracy improvement, spatiotemporal scale expansion, and dynamic trend quantification. To address these problems, this study proposed a new framework that progressed layer by layer f

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Bhakti, Deepak Kadam Ashwini Mangesh Deshpande. "Leveraging 3D convolutional networks for effective video feature extraction in video summarization." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1616–25. https://doi.org/10.11591/ijeecs.v37.i3.pp1616-1625.

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Video feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimension

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