Wide Range Head Pose Estimation Using a Single RGB Camera for Intelligent Surveillance

Rahmaniar, Wahyu; Haq, Qazi Mazhar ul; Lin, Ting-Lan

doi:10.1109/jsen.2022.3168863

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…There is no need to precompute specific facial features. A head pose estimation method for various facial conditions such as occlusion and challenging viewpoints was proposed in [27]. A combination of coarse and fine feature map classification was proposed to train a multi-loss deep convolutional neural network in order to get the exact Euler angles for the head position.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

“…In head pose estimation, the left-right symmetry information of the head can be well resolved in the roll and yaw dimensions of pose angle estimation, while the pitch dimension of head pose estimation requires the use of the relative geometric position relationships of salient regions of the face. Facial feature recognition head pose estimation methods are currently available based on traditional geometric model methods [15]- [19] and deep learning [20]- [27] based methods. The head pose estimation methods described above are geared towards a wide range of pose estimation, and the accuracy of pose estimation is still relatively coarse.…”

Section: Introductionmentioning

confidence: 99%

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Camper’s Plane Localization and Head Pose Estimation Based on Multi-View RGBD Sensors

et al. 2022

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Head pose estimation (HPE) is a key step in computation and quantification of 3D facial features and has a significant impact on the precision and accuracy of measurements. High-precision HPE is the basis for standardized facial data collection and analysis. The Camper's plane is the standard (baseline) plane commonly used by anthropologists for head and face research, but there is no research on automatic positioning of the Camper's plane using color and depth cameras. This paper presents a high-accuracy method for Camper's plane localization and HPE based on multi-view RGBD depth sensors. The 3D facial point clouds acquired by the multi-view RGBD depth sensors are aligned to obtain a complete 3D face. Keypoint RCNN is used for facial keypoint detection to obtain facial landmarks. A method is proposed to build a general face datum model based on a self-built dataset. The head pose is estimated by applying rigid body transformation to an individual 3D face and the general 3D face model. In order to verify the accuracy of Camper's plane localization and HPE, 102 cases of 3D facial data and experiments were collected and conducted. The tragus and nasal alar points are localized to within 7 pixels (about 0.83 cm) and the average accuracy of the three dimensions of Camper's plane identified is 0.87°, 0.64° and 0.47° respectively. The average accuracies of the three dimensions of HPE were 1.17°, 0.90° and 0.97. The experiment results demonstrate the effectiveness of the method for Camper's plane localization and HPE.INDEX TERMS Head pose estimation, Multi-view, Camper's plane, Depth sensor, 3-D point cloud.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Camper’s Plane Localization and Head Pose Estimation Based on Multi-View RGBD Sensors

et al. 2022

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“…Putro et al (2022) proposed a high-efficiency face detection algorithm that uses lighting to precisely locate faces [52]. According to Rahmaniar et al (2022), head posture estimation is used in several IVS systems, such as human behavior analysis, intelligent driver assistance, and visual warning and monitoring systems. These systems require precise alignment and prediction of head movements.…”

Section: Ivs In Background Modelingmentioning

confidence: 99%

Knowledge Development Trajectories of Intelligent Video Surveillance Domain: An Academic Study Based on Citation and Main Path Analysis

Huang,

Chen,

2024

Sensors

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Smart city is an area where the Internet of things is used effectively with sensors. The data used by smart city can be collected through the cameras, sensors etc. Intelligent video surveillance (IVS) systems integrate multiple networked cameras for automatic surveillance purposes. Such systems can analyze and monitor video data and perform automatic functions required by users. This study performed main path analysis (MPA) to explore the development trends of IVS research. First, relevant articles were retrieved from the Web of Science database. Next, MPA was performed to analyze development trends in relevant research, and g-index and h-index values were analyzed to identify influential journals. Cluster analysis was then performed to group similar articles, and Wordle was used to display the key words of each group in word clouds. These key words served as the basis for naming their corresponding groups. Data mining and statistical analysis yielded six major IVS research topics, namely video cameras, background modeling, closed-circuit television, multiple cameras, person reidentification, and privacy, security, and protection. These topics can boost the future innovation and development of IVS technology and contribute to smart transportation, smart city, and other applications. According to the study results, predictions were made regarding developments in IVS research to provide recommendations for future research.

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“…As a result, the correct margins on the face bounding box could improve the accuracy of calculating the angle of the head pose. Based on the head pose estimation in [20], YOLOv4 could detect face bounding boxes more precisely than other methods, such as Haarcascade and SSD-MobileNetV2. YOLOv4 could still detect facial areas precisely for various difficult positions even though the face is covered.…”

Section: A Face Detectionmentioning

confidence: 99%

“…The proposed head pose estimation used RGB images rather than depth information for individual color frames to obtain pixel-level intensities. This head pose detection is based on the method in [20] by simplifying the backbone and feature maps.…”

Section: Head Pose Estimationmentioning

confidence: 99%

Touchless Head-Control (THC): Head Gesture Recognition for Cursor and Orientation Control

Rahmaniar

Ma’arif

Lin

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The touchless techniques in humancomputer interaction (HCI) can effectively expand computer access capabilities for disabled people. This paper presents Touchless Head-Control (THC), an assistive system method for computer cursor control based on head pose captured with an RGB camera. Our work aimed to replace the standard cursor control using a device on the user's head. The convolutional neural networks with predicted fine-grained feature maps and binned classification were applied to estimate the head pose angles. The mouse pointer or cursor is moved to actual locations on the screen based on head movement (yaw and pitch) and the center position of the face. Head tilt to the right or left (roll) to control the mouse button. In addition, the proposed method can be used to simulate the movement of the robot or joystick using the head to control objects within three degrees of freedom (DOF). Various participants were involved in the interaction design evaluation, in which target selection accuracy, travel time, and path efficiency were measured. This technology allows people with limited motor skills to easily control a PC cursor and 3D object orientation without the use of additional equipment or sensors.

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Wide Range Head Pose Estimation Using a Single RGB Camera for Intelligent Surveillance

Cited by 9 publications

References 40 publications

Camper’s Plane Localization and Head Pose Estimation Based on Multi-View RGBD Sensors

Camper’s Plane Localization and Head Pose Estimation Based on Multi-View RGBD Sensors

Knowledge Development Trajectories of Intelligent Video Surveillance Domain: An Academic Study Based on Citation and Main Path Analysis

Touchless Head-Control (THC): Head Gesture Recognition for Cursor and Orientation Control

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