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| 1 | Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos显示文摘Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms. | Xiaohui Yuan Longbo Kong Dengchao Feng Zhenchun Wei | 2017 | IEEE/CAA Journal of Automatica Sinica2017，4，4： | 8 |
| 2 | Study on straw incineration monitoring based on the combination of remote sensing image显示文摘Straw incineration monitor is a key part of international environmental governance.In the paper,the combination of MODIS,MUX and TLC remote sensors is used to monitor straw burning fire points accurately.MODIS remote sensor has the characteristics of high temporal resolution and thermal infrared band,which can be used to judge the regional thermal abnormal variation and preliminary extract the suspicious thermal abnormal points.Combining with GIS information,the preliminary position of MODIS thermal abnormal points can be acquired.The MUX and TLC sensors of ZY-3 satellite in the preliminary position area can be pretreated,which includes radiometric calibration,atmospheric correction,geometric precision correction,ortho-rectification,etc.Through analyzing the physical properties and spectral information in the straw incineration area,the interpretation features of the straw incineration area will be determined.Then the high geographical resolution fusion image with two meters resolution can be interpreted,and the information of fire-point in high geographical resolution remote sensor can be extracted.Combining with the Google earth map to compare interpretation images in different time range of this area,and using ArcGIS platform to accurately position the confirmed fire point,the final position of the fire can be determined.Correspondingly,the combination of remote sensing sensors with high,medium and low resolution can be used to monitor the straw incineration point in county area.In experimental area,there are twenty-three straw burning fire points are found.The experimental results show that,this method can realize precise monitoring of straw incineration point in county area.However,straw incineration point monitoring in real time still need to be further investigated. | FENG Dengchao QIN Huanyu ZENG Yong | 2014 | Instrumentation2014，1，3： | 8 |
| 3 | Prediction and Analysis of O3 Based on the ARIMA Model显示文摘Despite of the small amount in the atmosphere,ozone is one of the most critical atmospheric component as it protects human beings and any other life on the earth from the sun's high frequency ultraviolet radiation. In recent decades,the global ozone depletion caused by human activities is w ell know n and produces an ' ozone hole',the most direct consequence of w hich is the increase in ultraviolet radiation,w hich w ill affect human survival,climatic environment,ecological environment and other important adverse impacts. Due to the implementation of the M ontreal protocol and other agreement,the total amount of ozone depleting substance in the atmosphere has been prominent reduced,w hich w ill lead to a new round of regional climate change.Therefore,predicting the changes of the total ozone in the future w ill have an important guiding significance for predicting the future climate change and making reasonable measures to deal w ith the climate change. In this paper,based on the ozone data of 1979 to 2016 in the southern hemisphere and ARIM A model algorithm,using time series analysis,w e obtain prediction effect of ARIM A model is good by Ljung-Box Q-test and R^2,and the model can be used to predict the future ozone change. With the help of SPSS softw are,the future trend of the total ozone can be predicted in the future 50 years. Based on the above experiment results,the global ozone change in the future 50 years can be forecasted,namely the atmospheric ozone layer w ill return to its 1980's standard by the middle of this century at the global scale. | FENG Dengchao LIANG Lishui LI Chunjiao | 2017 | Instrumentation2017，4，2： | 2 |
| 4 | A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform显示文摘Spatiotemporal residual noise in terrestrial earth observation products,often caused by unfavorable atmospheric conditions,impedes their broad applications.Most users prefer to use gap-filled remote sensing products with time series reconstruction(TSR)algorithms.Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources.This study introduces a new open-source software package entitled‘HANTS-GEE’that implements a well-known and robust TSR algorithm,i.e.Harmonic ANalysis of Time Series(HANTS),on the Google Earth Engine(GEE)platform for scalable reconstruction of terrestrial earth observation data.Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE.According to site-based and regional-based case evaluation,the new tool can effectively eliminate cloud contamination in the time series of earth observation data.Compared with traditional PC-based HANTS implementation,the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites.The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume,promoting algorithm usage by much broader user communities.To our knowledge,the software package is thefirst tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms. | Jie Zhou Massimo Menenti Li Jia Bo Gao Feng Zhao Yilin Cui Xuqian Xiong Xuan Liu Dengchao Li | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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