Vision-based Head Research Articles

Integrating vision-based head movement detection into a serious game towards game-based neck pain treatment

Integrating vision-based head movement detection into a serious game towards game-based neck pain treatment

Deteksi Helm Keselamatan Menggunakan Jetson Nano dan YOLOv7

Increasing awareness of the importance of head personal protective equipment in industrial and construction environments has become a major concern in efforts to improve occupational safety. This research developed an early detection system for the use of computer vision-based head protective equipment using the YOLOv7 model and the Jetson Nano controller. The YOLOv7 algorithm was chosen for its ability for fast and accurate object detection. The YOLOv7 model was trained with a total dataset of 2799 images and iterations of 100 epochs to detect head personal protective equipment with a high degree of accuracy. The system captures imagery, activates a warning alarm, and sends a notification to Telegram when a violation occurs on an object that is not wearing a safety helmet. The test results using the confusion matrix method showed that the developed system was able to detect head personal protective equipment with an accuracy rate of 97.23%, which shows the system's ability to recognize personal protective equipment with very high accuracy. In addition, the system also showed a precision value of 98.71% indicating that all detections performed were correct, and a recall of 95.63% which describes the system's ability to recognize most of the head personal protective equipment available. The average FPS result using GPU with CUDA on Jetson Nano reached 5,723 FPS.

A Portable Vision-Based Head Tracking Exergame Solution for Neck Rehabilitation

The current practice of neck rehabilitation is done through laser-pointing devices in a physical setup or Virtual Reality head-mounted devices, whereby the patient will control and navigate a laser beam to follow a specific pattern. These setups are cumbersome to the patient. We propose a portable computer vision-based marker-less head tracking exergame for neck rehabilitation. Our system retargets head postures to control a game object and considers neck mobility while incorporating movement modulation during posture overcompensation. This system would be tailored to the specific patient in the steps of automatic calibration and gameplay interaction. We used the head and body pose to calibrate the game scene dimensions, and the upper body joint angles to feedback the posture over-compensation in the form of gameplay variables augmentation. From our testing, it has shown that this is a viable exergame solution that could be adopted for neck exercise.

Improved Heart-Rate Measurement from Mobile Face Videos

Newtonian reaction to blood influx into the head at each heartbeat causes subtle head motion at the same frequency as the heartbeats. Thus, this head motion can be used to estimate the heart rate. Several studies have shown that heart rates can be measured accurately by tracking head motion using a desktop computer with a static camera. However, implementation of vision-based head motion tracking on smartphones demonstrated limited accuracy due to the hand-shaking problem caused by the non-static camera. The hand-shaking problem could not be handled effectively with only the frontal camera images. It also required a more accurate method to measure the periodicity of noisy signals. Therefore, this study proposes an improved head-motion-based heart-rate monitoring system using smartphones. To address the hand-shaking problem, the proposed system leverages the front and rear cameras available in most smartphones and dedicates each camera to tracking facial features that correspond to head motion and background features that correspond to hand-shaking. Then, the locations of facial features are adjusted using the average point of the background features. In addition, a correlation-based signal periodicity computation method is proposed to accurately separate the true heart-rate-related component from the head motion signal. The proposed system demonstrates improved accuracy (i.e., lower mean errors in heart-rate measurement) compared to conventional head-motion-based systems, and the accuracy is sufficient for daily heart-rate monitoring.

Model-free non-rigid head pose tracking by joint shape and pose estimation

Head pose estimation under non-rigid face movement is particularly useful in applications relating to eye-gaze tracking in less constrained scenarios, where the user is allowed to move naturally during tracking. Existing vision-based head pose estimation methods often require accurate initialisation and tracking of specific facial landmarks, while methods that handle non-rigid face deformations typically necessitate a preliminary training phase prior to head pose estimation. In this paper, we propose a method to estimate the head pose in real-time from the trajectories of a set of feature points spread randomly over the face region, without requiring a training phase or model-fitting of specific facial features. Conversely, our method exploits the 3-dimensional shape of the surface of interest, recovered via shape and motion factorisation, in combination with Kalman and particle filtering to determine the contribution of each feature point to the estimation of head pose based on a variance measure. Quantitative and qualitative results reveal the capability of our method in handling non-rigid face movement without deterioration of the head pose estimation accuracy.

Development of a Head Gesture Interface for a Self-portrait Camera

Most existing digital camera user interfaces place little emphasis on self- portrait options. Therefore, it is not always easy to take self-portraits using conventional user interfaces. This paper presents a vision-based head gesture interface for controlling a self-portrait camera that helps users to take self-portraits effectively and efficiently. Intu- itive nodding and head-shaking gestures control the camera zoom in/out on the face, and a mouth-opening gesture triggers the camera to take a picture. We evaluated its usability factors (effectiveness, efficiency, and satisfaction) and compared it to a remote control in a user study. The results suggest that our interface is useful for taking self-portrait pictures.

Active vision for controlling an electric wheelchair

Most of the electric wheelchairs available in the market are joystick-driven and therefore assume that the user is able to use his hand motion to steer the wheelchair. This does not apply to many users that are only capable of moving the head like quadriplegia patients. This paper presents a vision-based head motion tracking system to enable such patients of controlling the wheelchair. The novel approach that we suggest is to use active vision rather than passive to achieve head motion tracking. In active vision-based tracking, the camera is placed on the user's head rather than in front of it. This makes tracking easier, more accurate and enhances the resolution. This is demonstrated theoretically and experimentally. The proposed tracking scheme is then used successfully to control our electric wheelchair to navigate in a real world environment.

The IBM HeadTracking Pointer: Improvements in vision-based pointer control

Vision-based head trackers have been around for some years and are even beginning to be commercialized, but problems remain with respect to usability. Users without the ability to use traditional pointing devices – the intended audience of such systems – have no alternative if the automatic bootstrapping process fails. There is room for improvement in face tracking, and the pointer movement dynamics do not support accurate and efficient pointing. This paper describes the IBM HeadTracking Pointer, a system which attempts to directly address some of these issues. Head gestures are used to provide the end user a greater level of autonomous control over the system. A novel face-tracking algorithm reduces drift under variable lighting conditions, allowing the use of absolute, rather than relative, pointer positioning. Most importantly, the pointer dynamics have been designed to take into account the constraints of head-based pointing, with a non-linear gain which allows stability in fine pointer movement, high speed on long transitions and adjustability to support users with different movement dynamics. User studies have identified some difficulties with training the system and some characteristics of the pointer motion that take time to get used to, but also good user feedback and very promising performance results.

A simple vision-based head tracking method for eye-controlled human/computer interface

A simple vision-based head tracking method for eye-controlled human/computer interface

An efficient camera calibration method for vision-based head tracking

The aim of this study is to develop and evaluate an efficient camera calibration method for vision-based head tracking. Tracking head movements is important in the design of an eye-controlled human/computer interface. A vision-based head tracking system is proposed to allow the user's head movements in the design of the eye-controlled human/computer interface. We propose an efficient camera calibration method to track the three-dimensional position and orientation of the user's head accurately. We also evaluate the performance of the proposed method and the influence of the configuration of calibration points on the performance. The experimental error analysis results showed that the proposed method can provide more accurate and stable pose (i.e. position and orientation) of the camera than the direct linear transformation method which has been used in camera calibration. The results for this study can be applied to the tracking of head movements related to the eye-controlled human/computer interface and the virtual reality technology.