With the high-speed development of transportation industry,highway traffic safety has become a considerable problem.Meanwhile,with the development of embedded system and hardware chip,in recent years,human eye detecti...With the high-speed development of transportation industry,highway traffic safety has become a considerable problem.Meanwhile,with the development of embedded system and hardware chip,in recent years,human eye detection eye tracking and positioning technology have been more and more widely used in man-machine interaction,security access control and visual detection.In this paper,the high parallelism of FPGA was utilized to realize an elliptical approximate real-time human eye tracking system,which was achieved by the series register structure and random sample consensus(RANSAC),thus improving the speed of image processing without using external memory.Because eye images acquired by the camera often generate a lot of noises due to uneven light and dark background,the preprocessing technologies such as color conversion,image filtering,histogram modification and image sharpening were adopted.In terms of feature extraction of images,the eye tracking algorithm in this paper adopted seven-section rectangular eye tracking characteristic method,which increased a section between the mouth and the nose on the basis of the traditional six-section method,so its recognition accuracy is much higher.It is convenient for the realization of hardware parallel system in FPGA.Finally,aiming at the accuracy and real-time performance of the design system,a more comprehensive simulation test was carried out.The human eye tracking system was verified on DE2-115 multimedia development platform,and the performance of VGA(resolution:640×480)images of 8-bit grayscale was tested.The results showed that the detection speed of this system was about 47 frames per second under the condition that the detection rate of human face(front face,no inclination)was 93%,which reached the real-time detection level.Additionally,the accuracy of eye tracking based on FPGA system was more than 95%,and it has achieved ideal results in real-time performance and robustness.展开更多
Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosen...Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.展开更多
Early Warning Aircraft(EWA)are the main force for air detection and its Human-Machine Interface(HMI)should be designed to support task efficiency and safety.With the appli-cation of advanced input method and interface...Early Warning Aircraft(EWA)are the main force for air detection and its Human-Machine Interface(HMI)should be designed to support task efficiency and safety.With the appli-cation of advanced input method and interface design in EWA,little is known about their actual usability in terms of human factors and ergonomics.The aim of this study was to investigate the effects of the input method and display mode of the situation map on EWA reconnaissance task performance with different information complexities.Eighteen participants attended a three-factor within-subject design experiment with input method(touch screen and mouse),display mode of the situation map(color and grayscale),and information complexity(high and low)as the inde-pendent variables.Participant behavior performance,subjective workload,heart rate/heart rate variability,and eye movements were recorded as the dependent variables.The results suggest that a touch screen requires greater task completion time and has greater physical demands than mouse operation;however,it also facilitates information processing by reducing the average fixation time.Color mode significantly decreases saccade counts compared to grayscale mode and is considered more appropriate for target search tasks as it induces less visual search load.High information complexity produces significant negative effects on behavior performance and subjective workload.It also has significant interaction effects with input method on fixation and saccade counts.The findings have implications in the optimization design of Human–Machine Interface for EWA task systems.展开更多
文摘With the high-speed development of transportation industry,highway traffic safety has become a considerable problem.Meanwhile,with the development of embedded system and hardware chip,in recent years,human eye detection eye tracking and positioning technology have been more and more widely used in man-machine interaction,security access control and visual detection.In this paper,the high parallelism of FPGA was utilized to realize an elliptical approximate real-time human eye tracking system,which was achieved by the series register structure and random sample consensus(RANSAC),thus improving the speed of image processing without using external memory.Because eye images acquired by the camera often generate a lot of noises due to uneven light and dark background,the preprocessing technologies such as color conversion,image filtering,histogram modification and image sharpening were adopted.In terms of feature extraction of images,the eye tracking algorithm in this paper adopted seven-section rectangular eye tracking characteristic method,which increased a section between the mouth and the nose on the basis of the traditional six-section method,so its recognition accuracy is much higher.It is convenient for the realization of hardware parallel system in FPGA.Finally,aiming at the accuracy and real-time performance of the design system,a more comprehensive simulation test was carried out.The human eye tracking system was verified on DE2-115 multimedia development platform,and the performance of VGA(resolution:640×480)images of 8-bit grayscale was tested.The results showed that the detection speed of this system was about 47 frames per second under the condition that the detection rate of human face(front face,no inclination)was 93%,which reached the real-time detection level.Additionally,the accuracy of eye tracking based on FPGA system was more than 95%,and it has achieved ideal results in real-time performance and robustness.
基金supported in part by the US National Science Foundation(NSF)under Grants ECCS-1923163 and CNS-2107190through the Wireless Engineering Research and Education Center at Auburn University.
文摘Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.
基金co-supported by the National Natural Science Foundation of ChinaCivil Aviation Administration of China (No. U1733118)+1 种基金the National Natural Science Foundation of China (No. 71301005)the Aeronautical Science Foundation of China (No. 20181330002)
文摘Early Warning Aircraft(EWA)are the main force for air detection and its Human-Machine Interface(HMI)should be designed to support task efficiency and safety.With the appli-cation of advanced input method and interface design in EWA,little is known about their actual usability in terms of human factors and ergonomics.The aim of this study was to investigate the effects of the input method and display mode of the situation map on EWA reconnaissance task performance with different information complexities.Eighteen participants attended a three-factor within-subject design experiment with input method(touch screen and mouse),display mode of the situation map(color and grayscale),and information complexity(high and low)as the inde-pendent variables.Participant behavior performance,subjective workload,heart rate/heart rate variability,and eye movements were recorded as the dependent variables.The results suggest that a touch screen requires greater task completion time and has greater physical demands than mouse operation;however,it also facilitates information processing by reducing the average fixation time.Color mode significantly decreases saccade counts compared to grayscale mode and is considered more appropriate for target search tasks as it induces less visual search load.High information complexity produces significant negative effects on behavior performance and subjective workload.It also has significant interaction effects with input method on fixation and saccade counts.The findings have implications in the optimization design of Human–Machine Interface for EWA task systems.
