Distributed human 3D pose estimation and action recognition.

In this paper, we propose a distributed solution for3D human pose estimation using a RGBD camera network. Thekey feature of our method is a dynamic hybrid consensus filter(DHCF) is introduced to fuse the multiple view informationof cameras. In contrast to the centralized fusion solution,the DHCF algorithm can be used in a distributed network,which requires no central information fusion center. Therefore,the DHCF based fusion algorithm can benefit from manyadvantages of distributed network. We also show that theproposed fusion algorithm can handle the occlusion problemseffectively, and achieve higher action recognition rate comparedto the ones using only single view information

Visual SLAM based on dynamic object removal

Visual simultaneous localization and mapping (SLAM) is the core of intelligent robot navigation system. Many traditional SLAM algorithms assume that the scene is static. When a dynamic object appears in the environment, the accuracy of visual SLAM can degrade due to the interference of dynamic features of moving objects. This strong hypothesis limits the SLAM applications for service robot or driverless car in the real dynamic environment. In this paper, a dynamic object removal algorithm that combines object recognition and optical flow techniques is proposed in the visual SLAM framework for dynamic scenes. The experimental results show that our new method can detect moving object effectively and improve the SLAM performance compared to the state of the art methods

Impacts of sea-land and mountain-valley circulations on the air pollution in Beijing-Tianjin-Hebei (BTH): A case study

In the study, observational data analyses and the WRF-CHEM model simulations are used to investigate the role of sea-land and mountain-valley breeze circulations in a severe air pollution event occurred in Beijing-Tianjin-Hebei (BTH) during August 9-10, 2013. Both the wind observations and the model simulations have clearly indicated the evolution of the sea-land and mountain-valley breeze circulations during the event. The WRF-CHEM model generally reproduces the local meteorological circulations and also performs well in simulating temporal variations and spatial distributions of fine particulate matters (PM2.5) and ozone (O-3) concentrations compared to observations in BTH. The model results have shown that the offshore land breeze transports the pollutants formed in Shandong province to the Bohai Gulf in the morning, causing the formation of high O-3 and PM2.5 concentrations over the gulf. The onshore sea breeze not only causes the formation of a convergence zone to induce upward movement, mitigating the surface pollution to some degree, also recirculates the pollutants over the gulf to deteriorate the air quality in the coastal area. The upward valley breeze brings the pollutants in the urban area of Beijing to the mountain area in the afternoon, and the downward mountain breeze transports the pollutants back during nighttime. The intensity of the mountain-valley breeze circulation is weak compared to the land-sea breeze circulation in BTH. It is worth noting that the local circulations play an important role when the large-scale meteorological conditions are relatively weak. (C) 2017 Elsevier Ltd. All rights reserved

Simultaneous monocular visual odometry and depth reconstruction with scale recovery

In this paper, we propose a deep neural net-work that can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments

Simulations of summertime fossil fuel CO2 in the Guanzhong basin, China

Recent studies on fossil fuel CO2 simulation associated with Delta(CO2)-C-14 measurements is quite limited, particularly in China. In this study, the fossil fuel CO2 recently added to the atmosphere (delta CO(2)ff) over the Guanzhong basin, central China, during summer 2012 is simulated using a modified WRF-CHEM model constrained by measured CO2 mixing ratio and Delta(CO2)-C-14. The model well captures the temporal variation of observed CO2 mixing ratio and Delta(CO2)-C-14, and reasonably reproduces the distribution of observed Delta(CO2)-C-14. The simulation shows a significant variation of delta CO(2)ff during summertime, ranging from <5 ppmv to similar to 100 ppmv and no remarkable trend of delta CO(2)ff is found for June, July, and August. The delta CO(2)ff level is closely associated with atmospheric diffusion conditions. The diurnal cycle of delta CO(2)ff presents a double-peak pattern, a nocturnal one and a rush-hour one, related to the development of planetary boundary layer and CO2 emission from vehicles. The spatial distributions of summertime delta CO(2)ff within the basin is clearly higher than the outside, reaching up to 40 ppmv in urban Xi'an and 15 ppmv in its surrounding areas, indicative of large local fossil fuel emissions. Furthermore, we find that neglecting the influence of summer heterotrophic respiration in terrestrial biosphere would slightly underestimate the calculated delta CO(2)ff by about 0.38 ppmv in the basin. (C) 2017 Elsevier B.V. All rights reserved

Effect of ecological restoration programs on dust concentrations in the North China Plain: a case study

In recent decades, the Chinese government has made a great effort in initiating large-scale ecological restoration programs (ERPs) to reduce the dust concentrations in China, especially for dust storm episodes. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product, the ERP-induced land cover changes are quantitatively evaluated in this study. Two obvious vegetation protective barriers arise throughout China from the southwest to the northeast, which are well known as the "Green Great Wall" (GGW). Both the grass GGW and forest GGW are located between the dust source region (DSR) and the densely populated North China Plain (NCP). To assess the effect of ERPs on dust concentrations, a regional transport/dust model (WRF-DUST, Weather Research and Forecast model with dust) is applied to investigate the evolution of dust plumes during a strong dust storm episode from 2 to 8 March 2016. The WRF-DUST model generally performs reasonably well in reproducing the temporal variations and spatial distributions of near-surface [PMC] (mass concentration of particulate matter with aerodynamic diameter between 2.5 and 10 mu m) during the dust storm event. Sensitivity experiments have indicated that the ERP-induced GGWs help to reduce the dust concentration in the NCP, especially in BTH (Beijing, Tianjin, and Hebei). When the dust storm is transported from the upwind DSR to the downwind NCP, the [PMC] reduction ranges from -5 to -15% in the NCP, with a maximum reduction of -12.4% (-19.2 mu gm(3)) in BTH and -7.6% (-10.1 mu g m(3)) in the NCP. We find the dust plumes move up to the upper atmosphere and are transported from the upwind DSR to the downwind NCP, accompanied by dust decrease. During the episode, the forest GGW is nonsignificant in dust concentration control because it is of benefit for dry deposition and not for emission. Conversely, the grass GGW is beneficial in controlling dust erosion and is the dominant reason for [PMC] decrease in the NCP. Because the air pollution is severe in eastern China, especially in the NCP, and the contribution of dust episodes is significant, the reduction of dust concentrations will have important effects on severe air pollution. This study illustrates the considerable contribution of ERPs to the control of air pollution in China, especially in springtime

Online human action recognition with spatial and temporal skeleton features using a distributed camera network

Online action recognition is an important task for human-centered intelligent services. However, it remains a highly challenging problem due to the high varieties and uncertainties of spatial and temporal scales of human actions. In this paper, the following core ideas are proposed to deal with the online action recognition problem. First, we combine spatial and temporal skeleton features to represent human actions, which include not only geometrical features, but also multiscale motion features, such that both spatial and temporal information of the actions are covered. We use an efficient one-dimensional convolutional neural network to fuse spatial and temporal features and train them for action recognition. Second, we propose a group sampling method to combine the previous action frames and current action frames, which are based on the hypothesis that the neighboring frames are largely redundant, and the sampling mechanism ensures that the long-term contextual information is also considered. Third, the skeletons from multiview cameras are fused in a distributed manner, which can improve the human pose accuracy in the case of occlusions. Finally, we propose a Restful style based client-server service architecture to deploy the proposed online action recognition module on the remote server as a public service, such that camera networks for online action recognition can benefit from this architecture due to the limited onboard computational resources. We evaluated our model on the data sets of JHMDB and UT-Kinect, which achieved highly promising accuracy levels of 80.1% and 96.9%, respectively. Our online experiments show that our memory group sampling mechanism is far superior to the traditional sliding window