A Danger Estimation System for Infants Based on a Foreground Dynamic Probability Model

This study presents a danger estimation system to prevent accidents among infants. A video camera positioned above the infant＇s crib captures video. The proposed system can monitor the behavior of infants aged zero to six months. If there is a change in behavior or any other unusual occurrence, the system alerts the person responsible to attend to the baby immediately. The proposed system operates in three phases, which are foreground color model （FC model） construction, infant detection, and degree of danger analysis. During FC model construction, the foreground color histogram is created iteratively; the background image does not have to be constructed first. A motion-history image （MHI） is also obtained based on the motion of the infant. The color and motion information supplied by the FC model and the MHI are combined to detect the infant, who is regarded as the foreground object in the input frame. Moreover, six features of infant behavior are extracted from the detected infant to measure the degree of danger faced by the infant, and the result is used to warn the baby-sitter if needed. Experimental results show that the proposed method is robust and efficient.

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.

Impact of Wavelength-Routed Network Physical Topology on Blocking Probability Using a Dynamic Traffic Growth Model

We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.

A novel variable-lag probability hypothesis density smoother for multi-target tracking

It is understood that the forward-backward probability hypothesis density （PHD） smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show that they cannot give a good cardinality （i.e., the number of targets） estimate. This is because backward smoothing ignores the effect of temporary track drop- ping caused by forward filtering and/or anomalous smoothing resulted from deaths of targets. To cope with such a problem, a novel PHD smoothing algorithm, called the variable-lag PHD smoother, in which a detection process used to identify whether the filtered cardinality varies within the smooth lag is added before backward smoothing, is developed here. The analytical results show that the proposed smoother can almost eliminate the influences of temporary track dropping and anomalous smoothing, while both the cardinality and the state estimations can significantly be improved. Simulation results on two multi-target tracking scenarios verify the effectiveness of the proposed smoother.

Explicating the mechanisms of land cover change in the New Eurasian Continental Bridge Economic Corridor region in the 21st century

Land cover change has presented clear spatial differences in the New Eurasian Continental Bridge Economic Corridor(NECBEC)region in the 21 st century.A spatiotemporal dynamic probability model and a driving force analysis model of land cover change were developed to analyze explicitly the dynamics and driving forces of land cover change in the NECBEC region.The results show that the areas of grassland,cropland and built-up land increased by 114.57 million ha,8.41 million ha and 3.96 million ha,and the areas of woodland,other land,and water bodies and wetlands decreased by 74.09 million ha,6.26 million ha,and 46.59 million ha in the NECBEC region between 2001 and 2017,respectively.Woodland and other land were mainly transformed to grassland,and grassland was mainly transformed to woodland and cropland.Built-up land had the largest annual rate of increase and 50%of this originated from cropland.Moreover,since the Belt and Road Initiative(BRI)commenced in 2013,there has been a greater change in the dynamics of land cover change,and the gaps in the socio-economic development level have gradually decreased.The index of socio-economic development was the highest in western Europe,and the lowest in northern Central Asia.The impacts of socio-economic development on cropland and built-up land were greater than those for other land cover types.In general,in the context of rapid socio-economic development,the rate of land cover change in the NECBEC has clearly shown an accelerating trend since 2001,especially after the launch of the BRI in 2013.