Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional ...Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.展开更多
Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has recei...Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has received significant attention from vehicle safety analysts.However,pedestrian protection in parking lots still faces many challenges.For example,the physical structure of a parking lot may be complex,and dead corners would occur when the vehicle density is high.These lead to pedestrians’sudden appearance in the vehicle’s path from an unexpected position,resulting in collision accidents in the parking lot.We advocate that besides vehicular sensing data,high-precision digital map of the parking lot,pedestrians’smart device’s sensing data,and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot.However,this subject has not been studied and explored in existing studies.Tofill this void,this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces.We also evaluate the proposed method through real-world experiments.The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy.It can also be used for pedestrian tracking in parking spaces.展开更多
This paper shows the method of estimating spatiotemporal distribution of pedestrians by using watch cameras. We estimate the distribution without tracking technology, with pedestrian＇s privacy protected and in Umeda ...This paper shows the method of estimating spatiotemporal distribution of pedestrians by using watch cameras. We estimate the distribution without tracking technology, with pedestrian＇s privacy protected and in Umeda underground mall. Lately spatiotemporal distribution of pedestrians has being increasingly important in the field of urban planning, disaster prevention planning, marketing and so on. Although many researchers have tried to capture the information of location as dealing with some sensors, some problems still remain, such as the investment of sensors, the restriction of the number of people who has the device they are able to capture. From such background, we develop an original labelling algorithm and estimate the spatiotemporal distribution of pedestrians and the information of the passing time and the direction of pedestrians from sequential images of a watch camera.展开更多
基金supported by the National Natural Science Foundation of China under(Grant No.52175531)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant(Grant Nos.KJQN202000605 and KJZD-M202000602)。
文摘Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.
基金Our research in this paper was partially supported by JST COI JPMJCE1317.
文摘Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has received significant attention from vehicle safety analysts.However,pedestrian protection in parking lots still faces many challenges.For example,the physical structure of a parking lot may be complex,and dead corners would occur when the vehicle density is high.These lead to pedestrians’sudden appearance in the vehicle’s path from an unexpected position,resulting in collision accidents in the parking lot.We advocate that besides vehicular sensing data,high-precision digital map of the parking lot,pedestrians’smart device’s sensing data,and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot.However,this subject has not been studied and explored in existing studies.Tofill this void,this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces.We also evaluate the proposed method through real-world experiments.The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy.It can also be used for pedestrian tracking in parking spaces.
基金Partially Supported by Grant-in-Aid for Scientific Research(A)(No.25240004)
文摘This paper shows the method of estimating spatiotemporal distribution of pedestrians by using watch cameras. We estimate the distribution without tracking technology, with pedestrian＇s privacy protected and in Umeda underground mall. Lately spatiotemporal distribution of pedestrians has being increasingly important in the field of urban planning, disaster prevention planning, marketing and so on. Although many researchers have tried to capture the information of location as dealing with some sensors, some problems still remain, such as the investment of sensors, the restriction of the number of people who has the device they are able to capture. From such background, we develop an original labelling algorithm and estimate the spatiotemporal distribution of pedestrians and the information of the passing time and the direction of pedestrians from sequential images of a watch camera.
