8 research outputs found
Low-cost eye-tracking for human computer interaction
Knowing the user\u27s point of gaze has long held the promise of being a useful methodology for human computer interaction. However, a number of barriers have stood in the way of the integration of eye tracking into everyday applications, including the intrusiveness, robustness, availability, and price of eye-tracking systems. The goal of this thesis is to lower these barriers so that eye tracking can be used to enhance current human computer interfaces. An eye-tracking system was developed. The system consists of an open-hardware design for a digital eye tracker that can be built from low-cost off-the-shelf components, and a set of open-source software tools for digital image capture, manipulation, and analysis in eye-tracking applications. Both infrared and visible spectrum eye-tracking algorithms were developed and used to calculate the user\u27s point of gaze in two types of eye tracking systems, head-mounted and remote eye trackers. The accuracy of eye tracking was found to be approximately one degree of visual angle. It is expected that the availability of this system will facilitate the development of eye-tracking applications and the eventual integration of eye tracking into the next generation of everyday human computer interfaces
Group Equivariant BEV for 3D Object Detection
Recently, 3D object detection has attracted significant attention and
achieved continuous improvement in real road scenarios. The environmental
information is collected from a single sensor or multi-sensor fusion to detect
interested objects. However, most of the current 3D object detection approaches
focus on developing advanced network architectures to improve the detection
precision of the object rather than considering the dynamic driving scenes,
where data collected from sensors equipped in the vehicle contain various
perturbation features. As a result, existing work cannot still tackle the
perturbation issue. In order to solve this problem, we propose a group
equivariant bird's eye view network (GeqBevNet) based on the group equivariant
theory, which introduces the concept of group equivariant into the BEV fusion
object detection network. The group equivariant network is embedded into the
fused BEV feature map to facilitate the BEV-level rotational equivariant
feature extraction, thus leading to lower average orientation error. In order
to demonstrate the effectiveness of the GeqBevNet, the network is verified on
the nuScenes validation dataset in which mAOE can be decreased to 0.325.
Experimental results demonstrate that GeqBevNet can extract more rotational
equivariant features in the 3D object detection of the actual road scene and
improve the performance of object orientation prediction.Comment: 8 pages,3 figures,accepted by International Joint Conference on
Neural Networks (IJCNN)202
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal
Human gesture recognition using millimeter wave (mmWave) signals provides
attractive applications including smart home and in-car interface. While
existing works achieve promising performance under controlled settings,
practical applications are still limited due to the need of intensive data
collection, extra training efforts when adapting to new domains (i.e.
environments, persons and locations) and poor performance for real-time
recognition. In this paper, we propose DI-Gesture, a domain-independent and
real-time mmWave gesture recognition system. Specifically, we first derive the
signal variation corresponding to human gestures with spatial-temporal
processing. To enhance the robustness of the system and reduce data collecting
efforts, we design a data augmentation framework based on the correlation
between signal patterns and gesture variations. Furthermore, we propose a
dynamic window mechanism to perform gesture segmentation automatically and
accurately, thus enable real-time recognition. Finally, we build a lightweight
neural network to extract spatial-temporal information from the data for
gesture classification. Extensive experimental results show DI-Gesture achieves
an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments
and locations, respectively. In real-time scenario, the accuracy of DI-Gesutre
reaches over 97% with average inference time of 2.87ms, which demonstrates the
superior robustness and effectiveness of our system.Comment: The paper is submitted to the journal of IEEE Transactions on Mobile
Computing. And it is still under revie
Low-cost eye-tracking for human computer interaction
Knowing the user's point of gaze has long held the promise of being a useful methodology for human computer interaction. However, a number of barriers have stood in the way of the integration of eye tracking into everyday applications, including the intrusiveness, robustness, availability, and price of eye-tracking systems. The goal of this thesis is to lower these barriers so that eye tracking can be used to enhance current human computer interfaces. An eye-tracking system was developed. The system consists of an open-hardware design for a digital eye tracker that can be built from low-cost off-the-shelf components, and a set of open-source software tools for digital image capture, manipulation, and analysis in eye-tracking applications. Both infrared and visible spectrum eye-tracking algorithms were developed and used to calculate the user's point of gaze in two types of eye tracking systems, head-mounted and remote eye trackers. The accuracy of eye tracking was found to be approximately one degree of visual angle. It is expected that the availability of this system will facilitate the development of eye-tracking applications and the eventual integration of eye tracking into the next generation of everyday human computer interfaces.</p