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Leveraging Transfer Learning for spatio-temporal Human Activity Recognition from Video Sequences

Computers, Materials & Continua 2023年第3期74卷 5017-5033页

作者： Umair Muneer Butt Hadiqa Aman Ullah Sukumar Letchmunan Iqra Tariq Fadratul Hafinaz Hassan Tieng Wei Koh School of Computer Sciences Universiti Sains MalaysiaPenang1180Malaysia Department of Computer Science The University of ChenabGujrat50700Pakistan Department of Software Engineering and Information System Universiti Putra MalaysiaSelangor43400Malaysia

Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.

关键词： Human activity recognition deep learning transfer learning neural network ensemble learning spatio-temporal

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Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

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Journal of Central South University 2017年第10期24卷 2438-2448页

作者：徐枫石岩邓敏龚健雅刘启亮金瑞 Department of Geo-informatics Central South UniversityChangsha 410083China State Key Laboratory of Information Engineering in Surveying(Mapping&Remote Sensing Wuhan University)Wuhan 430079China Collaborative Innovation Center of Geospatial Technology Wuhan UniversityWuhan 430079China

Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.

关键词： climate sequences anomalous climatic events spatio-temporal teleconnection patterns multi-scale regionalization

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spatio-temporal evolution of Beijing 2003 SARS epidemic

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Science China Earth Sciences 2010年第7期53卷 1017-1028页

作者： CAO ZhiDong ZENG DaJun ZHENG XiaoLong WANG QuanYi WANG FeiYue WANG JinFeng WANG XiaoLi Key Laboratory of Complex Systems and Intelligence Science Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Center for Disease Control and Prevention Beijing 100013China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.

关键词： severe acute respiratory syndrome (SARS) Beijing morbidity rate spatial analysis spatio-temporal evolution control measures

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Tracking the in vivo spatio-temporal patterns of neovascularization via NIR-II fluorescence imaging

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Nano Research 2020年第11期13卷 3123-3129页

作者： Mo Chen Sijia Feng Yimeng Yang Yunxia Li Jian Zhang Shiyi Chen Jun Chen Institute of Sports Medicine of Fudan University Department of Orthopaedic Sports MedicineHuashan HospitalFudan UniversityShanghai200041China

The in vivo spatio-temporal patterns of neovascularization are still poorly understood because it is limited to multi-scale techniques from the cellular level to living animal level.Owing to deep tissue-penetration and zero autofluorescence background,the second near-infrared(NIR-II,1,000–1,700 nm)fluorescence imaging recently shows promise in breaking through this dilemma by dynamically tracking the pathophysiological process of neovascularization in vivo.Here,NIR-II fluorescence imaging was recruited for monitoring blood vessels in order to visualize the vascular injury and quantitively assess neovascularization in mouse models of acute skeleton muscle contusion and hindlimb ischemia.The temporal analysis of real-time NIR-II fluorescence intensity demonstrated that the blood flow perfusion of ischemia area was able to rapidly restore to 96%of pre-ischemic state within one week.Moreover,the spatial analysis revealed that the lower and outer quadrants of ischemia area in the mouse model of hindlimb ischemia always had relatively high blood flow perfusion compared with other quadrants during three weeks post-ischemia,and even exceeded pre-ischemic quantity at 21 days post-ischemia.In conclusion,this in vivo imaging technique has significant potential utility for studying the spatio-temporal patterns of neovascularization in vivo.

关键词： bioimaging second near-infrared(NIR-II)fluorescence nanoprobes spatio-temporal neovascularization

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A verification framework for spatio-temporal consistency language with CCSL as a specification language

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Frontiers of Computer Science 2020年第1期14卷 105-129页

作者： Yuanrui ZHANG Frédéric MALLET Yixiang CHEN MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal UniversityShanghai 200062China University Nice Sophia Antipolis I3SUMR 7271 CNRSINRIA06900 Sophia AntipolisFrance

The spatio-temporal Consistency Language(STeC)is a high-level modeling language that deals natively with spatio-temporal behaviour,i.e.,behaviour relating to certain locations and time.Such restriction by both locations and time is of first importance for some types of real-time systems.CCSL is a formal specification language based on logical clocks.It is used to describe some crucial safety properties for real-time systems,due to its powerful expressiveness of logical and chronometric time constraints.We consider a novel verification framework combining STeC and CCSL,with the advantages of addressing spatio-temporal consistency of system behaviour and easily expressing some crucial time constraints.We propose a theory combining these two languages and a method verifying CCSL properties in STeC models.We adopt UPPAAL as the model checking tool and give a simple example to illustrate how to carry out verification in our framework.

关键词： spatio-temporal consistency real-time systems spatio-temporal systems high-level modelling language clock constraint specification model checking verification framework

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Analysis of spatio-temporal pattern and driving force of land cover change using multi-temporal remote sensing images

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Science China(Technological Sciences) 2010年第S1期53卷 111-119页

作者： ZHOU QiMing & SUN Bo Department of Geography,Hong Kong Baptist University,Hong Kong,China Department of Geography Hong Kong Baptist University Hong Kong China

Landuse and land cover change is regarded as a good indicator that represents the impact of human activities on earth’s environment.When the large collection of multi-temporal satellite images has become available,it is possible to study a long-term historical process of land cover change.This study aims to investigate the spatio-temporal pattern and driving force of land cover change in the Pearl River Delta region in southern China,where the rapid development has been witnessed since 1980s.The fast economic growth has been associated with an accelerated expansion of urban landuse,which has been recorded by historical remote sensing images.This paper reports the method and outcome of the research that attempts to model spatio-temporal pattern of land cover change using multi-temporal satellite images.The classified satellite images were compared to detect the change from various landuse types to built-up areas.The trajectories of land cover change have then been established based on the time-series of the classified land cover classes.The correlation between the expansion of built-up areas and selected economic data has also been analysed for better understanding on the driving force of the rapid urbanisation process.The result shows that,since early 1990s,the dominant trend of land cover change has been from farmland to urban landuse.The relationship between economic growth indicator(measured by GDP)and built-up area can well fit into a linear regression model with correlation coefficients greater than 0.9.It is quite clear that cities or towns have been sprawling in general,demonstrating two growth models that were closely related to the economic development stages.

关键词： remote sensing change detection multi-temporal image processing spatio-temporal analysis land cover change Pearl River Delta urban expansion

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spatio-temporal Generative Adversarial Networks

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Chinese Journal of Electronics 2020年第4期29卷 623-631页

作者： QIN Chao GAO Xiaoguang School of Electronics and Information Engineering Northwestern Polytechnical University

We designed a spatiotemporal generative adversarial network which given some initial data and random noise, generates a consecutive sequence of spatiotemporal samples that have a logical relationship.We build spatial discriminators and temporal discriminators to distinguish whether the samples generated by the generator meet the requirements for time and space coherence.The model is trained on the skeletal dataset and the Caltrans Performance Measurement System District 7 dataset.In contrast to traditional Generative adversarial networks(GANs), the proposed spatiotemporal GAN can generate logically coherent samples with the corresponding spatial and temporal features while avoiding mode collapse.In addition, we show that our model can generate different styles of spatiotemporal samples given different random noise inputs.This model will extend the potential range of applications of GANs to areas such as traffic information simulations and multiagent adversarial simulations.

关键词： spatio-temporal Generative adversarial networks(GANs) Spatial discriminator temporal discriminator

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spatio-temporal changes of underground coal fires during 2008-2016 in Khanh Hoa coal field(North-east of Viet Nam) using Landsat time-series data

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Journal of Mountain Science 2018年第12期15卷 2703-2720页

作者： Tuyen Danh VU Thanh Tien NGUYEN Department of Training Hanoi University of Natural Resources and Environment Faculty of Surveying Mapping and Geographic Information Hanoi University of Natural Resources and Environment

Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.

关键词： Underground coal fires spatio-temporal changes Khanh Hoa coal field (Viet Nam) Landsat time-series data

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spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015

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Frontiers of Earth Science 2019年第1期13卷 92-110页

作者： Yongfeng WANG Zhaohui XUE Jun CHEN Guangzhou CHEN School of Environment and Engineering Anhui Jianzhu UniversityHefei 230022China School of Earth Sciences and Engineering Hohai UniversityNanjing 211100China

Phenology has become a good indicator for illustrating the long-term changes in the natural resources of the Yangtze River Delta.However,two issues can be observed from previous studies.On the one hand,existing time-series classification methods mainly using a single classifier,the discrimination power,can become deteriorated due to fluctuations characterizing the time series.On the other hand,previous work on the Yangtze River Delta was limited in the spatial domain (usually to 16 cities)and in the temporal domain (usually 2000-2010).To address these issues,this study attempts to analyze the spatiotemporal variation in phenology in the Yangtze River Delta (with 26 cities,enlarged by the state council in June 2016), facilitated by classifying the land cover types and extracting the phenological metrics based on Moderate Resolution Imaging Spectrometer (MODIS)Normalized Difference Vegetation Index (NDVI)time series collected from 2001 to 2015.First,ensemble learning (EL)-based classifiers are used for land cover classification,where the training samples (a total of 201,597)derived from visual interpretation based on GlobelLand30 are further screened using vertex component analysis (VCA),resulting in 600 samples for training and the remainder for validating. Then,eleven phenological metrics are extracted by TIMESAT (a package name)based on the time series, where a seasonal-trend decomposition procedure based on loess (STL-decomposition)is used to remove spikes and a Savitzky-Golay filter is used for filtering.Finally,the spatio-temporal phenology variation is analyzed by considering the classification maps and the phenological metrics.The experimental results indicate that:1)random forest (R.F)obtains the most accurate classification map (with an overall accuracy higher than 96%);2)different land cover types illustrate the various seasonalities;3)the Yangtze River Delta has two obvious regions,i.e.,the north and the south parts,resulting from different rainfall, temperature,and ecosystem conditions;4)the phenology

关键词： Yangtze River Delta MODIS NDVI ensemble learning,land cover classification spatio-temporal phenology

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spatio-temporal Evolution of Marine Fishery Industry Ecosystem Vulnerability in the Bohai Rim Region

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Chinese Geographical Science 2019年第6期29卷 1052-1064页

作者： LI Bo JIN Xiaoming Center for Studies of Marine Economy and Sustainable Development Liaoning Normal University

The building of the ocean power strategy and the implementation of the blue agriculture plan urgently need to strengthen the sustainable development of marine fishery.Taking vulnerability as the starting point, this paper constructs the vulnerability index system of marine fishery industry ecosystem from the aspects of sensitivity and response capacity, and combines the entropy method with the Topsis to comprehensively analyze the spatio-temporal evolution characteristics of vulnerability of marine fishery industry ecosystem in the Bohai Rim Region from 2001 to 2015.The results show that: 1) In the time dimension, from 2001 to 2015, the vulnerability of the marine fishery industry ecosystem in the Bohai Rim Region shows a fluctuant and degressive trend;2) In the spatial dimension, the spatial distribution of the marine fishery industry ecosystem vulnerability in the Bohai Rim Region presents the gradient characteristics which shows high vulnerability in the east and low vulnerability in the west.According to the evolution track of the system’s vulnerability level, the vulnerability of the marine fishery industry ecosystem is divided into ‘declining’ and ‘stable’ types of evolutionary structures;3) The development of marine fishery in the Bohai Rim Region needs to be derived from the marine fishery’s ecological environment and the industrial development mode and structure, which can improve the marine environment remediation efforts, optimize the marine fishery industry structure, vigorously focus on pelagic fishery, and enhance the introduction of marine fishery’s science and technology talents, etc.Then, the marine fishery’s development in the Bohai Rim Region will be moving in the green, circular and sustainable direction.

关键词： marine fishery marine industry ecosystem vulnerability spatio-temporal evolution Bohai Rim Region

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