Browsing by Author "Wu, Bing-Fei"

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A Contactless Sport Training Monitor Based on Facial Expression and Remote-PPG
(2017-01-01)
Wu, Bing-Fei
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Lin, Chun-Hsien
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Huang, Po-Wei
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Lin, Tzu-Min
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Chung, Meng-Liang
To successfully increase athletes' or exercisers' fitness and endurance, the factors of physiological signal, emotion, or the level of fatigue should be considered during the training program. Many clinical decision support systems can assist to monitor the exercisers by some wearable devices. And, the questionnaire should also be taken into account to produce a report. Such process is cumbersome, and the results are not objective. Furthermore, one may feel uncomfortable when wearing the devices during the training program. In this research, the Rating of Perceived Exertion (RPE) is expected to be estimated automatically without any wearable devices and questionnaires. A camera based heart rate detection algorithm and a fatigue expression feature extractor are fused to estimate the RPE value. The results show that our heart rate detection algorithm can be competitive to the wearable devices, and the trend of the detected heart rate is correlated to RPE. Moreover, the fatigue feature can help reduce the error of the estimation.
A Feature Selection Method for Vision-based Blood Pressure Measurement
(2018-01-01)
Fang, Yu-Fan
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Huang, Po-Wei
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Chung, Meng-Liang
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Wu, Bing-Fei
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電機工程學系
In this paper we investigate the latest vision-based method for systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurement. However, constantly blood pressure supervision needs sufficient medical equipment and may require the potential patients to tie a cuff, which is extremely inconvenient for them. What's more, continuously blood pressure measuring requires the patients to stay in the hospital and professional personnel to stand by. From the research before, we have learned that photoplethysmography (PPG) can be used to measure the blood pressure, which is known as cuffless blood pressure measurement. However, for the neonate and patients with empyrosis, photoplethysmography measuring device is still less practical and restricted in use due to the necessary contact for it to measure the systolic and diastolic blood pressure. Certain level of discomfort is still unavoidable with the use of PPG. We thus focus on remote PPG (rPPG); with green red difference (GRD) and Euler video magnification (EVM) and finite impulse response (FIR) bandpass filters, we are able to recover PPG signals from remote photoplethysmography. We propose a feature extraction measuring methods which yields a root mean square error for SBP as 11.22 mmHg and 7.83 mmHg for pulse pressure (PP) combined with the ANN model. For comparison, we've also used K nearest neighbor (KNN) and deep belief network-deep neural network (DBN-DNN).
A Motion Robust Remote-PPG Approach to Driver's Health State Monitoring
(2017-01-01)
Wu, Bing-Fei
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Chu, Yun-Wei
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Huang, Po-Wei
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Chung, Meng-Liang
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Lin, Tzu-Min
With the surging significance of personal health care, driver's physiological state is no longer negligible nowadays. Among all the indicators of health state in human, heart rate (HR) is one of the most cardinal indicators. The commonly used HR measurement is contact-type, might result in driver's distraction and discomfort in the vehicle applications. To cope with this problem, remote photoplethysmography (rPPG) is utilized to monitor HR at a distance via a web camera. Nevertheless, the rPPG is not without its flaw. The main concern of the rPPG technique is the potential not-robustness result from the arbitrary motion. Consequently, the contribution of this paper is to conquer the motion noise when the car is driving and the driver's health state is well monitored to enhance the public safety. The proposed algorithm is investigated in not only the indoor environment but as well the outdoor driving, which contains much more unpredictable motion. With k-nearest neighbor (kNN) classifier on chrominance-based features, the mean square error can be reduced from 30.6 to 2.79 bpm, approaching the medical instrument level. The proposed method can be applied to human improving driving safety for Advanced Driver Assistance Systems.
A multi-plane approach for text segmentation of complex document images
(2009-07-01)
Chen, Yen-Lin
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Wu, Bing-Fei
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電控工程研究所
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Institute of Electrical and Control Engineering
This study presents a new method, namely the multiplane segmentation approach, for segmenting and extracting textual objects from various real-life complex document images. The proposed multi-plane segmentation approach first decomposes the document image into distinct object planes to extract and separate homogeneous objects including textual regions of interest, non-text objects such as graphics and pictures, and background textures. This process consists of two stages-localized histogram multilevel thresholding and multi-plane region matching and assembling. Then a text extraction procedure is applied Oil the resultant planes to detect and extract textual objects with different characteristics in the respective planes. The proposed approach processes document images regionally and adaptively according to their respective local features. Hence detailed characteristics of the extracted textual objects, Particularly small characters with thin strokes, as well as gradational illuminations of characters, can be well-preserved. Moreover, this way also allows background objects with uneven, gradational, and sharp variations in contrast, illumination, and texture to be handled easily and well. Experimental results on real-life complex document images demonstrate that the proposed approach is effective in extracting textual objects with Various illuminations, sizes, and font styles from various types of complex document images. (C) 2008 Elsevier Ltd. All rights reserved.
A New Approach to Video-Based Traffic Surveillance Using Fuzzy Hybrid Information Inference Mechanism
(2013-03-01)
Wu, Bing-Fei
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Kao, Chih-Chung
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Juang, Jhy-Hong
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Huang, Yi-Shiun
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電控工程研究所
This study proposes a new approach to video-based traffic surveillance using a fuzzy hybrid information inference mechanism (FHIIM). The three major contributions of the proposed approach are background updating, vehicle detection with block-based segmentation, and vehicle tracking with error compensation. During background updating, small-range updating is adopted to overcome environmental changes under congested conditions. During vehicle detection, the proposed approach detects the vehicle candidates from the foreground image, and it resolves problems such as headlight effects. The tracking technique is employed to track vehicles in consecutive frames. First, the method detects edge features in congested scenes. Next, FHIIM is employed to determine the tracked vehicles. Finally, a method that compensates for error cases under congested conditions is applied to refine the tracking qualities. In our experiments, we tested scenarios both inside and outside the tunnel with three lanes. The results showed that the proposed system exhibits good performance under congested conditions.
A New Criterion of Human Comfort Assessment for Wheelchair Robots by Q-Learning Based Accompanist Tracking Fuzzy Controller
(2016-12)
Wu, Bing-Fei
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Chen, Po-Yen
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Lin, Chun-Hsien
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電控工程研究所
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Institute of Electrical and Control Engineering
Wheelchair users often struggle to drive safely and accompanied by caregivers. Wheelchair robots can employ the autonomous functions to avoid obstacles and reduce the workload of the caregivers. To achieve the goal, the accompanist needs to be steadily recognized and tracked by the robots. By using Q-learning based accompanist tracking fuzzy controller, wheelchair robots can peacefully follow the accompanist. Meanwhile, it is important to make the people on wheelchair robots feel comfortable. Based on ISO 2631-1, the ride qualities are obtained by averaging the acceleration data in the frequency bands, and the critical thresholds utilizing to assess the ride comfort are also determined inside. To make the level of bumpiness be as multiple as possible, four kinds of pavements are chosen in the experiments. The results show that the feelings of the occupants on the wheelchair robot are quite different from the ride comfort defined in ISO 2631-1, so a new standard is proposed to assess the ride comfort in this study. The accuracy of the proposed standard is 90.67 %, which is higher than that of ISO 2631-1, 42.48 %. Furthermore, to the best of our knowledge, this paper is thought to be the first one to present the ISO 2631-1-based comfort criterion for wheelchair robots.
A new vehicle detection approach in traffic jam conditions
(2007)
Wu, Bing-Fei
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Juang, Jhy-Hong
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Tsai, Ping-Tsung
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Chang, Ming-Wei
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Fan, Chung-Jui
The well-known vehicle detectors utilize the background extraction methods to segment the moving objects. The back-ground updating concept is applied to overcome the luminance variation which results in the error detection. These systems will meet a challenge when detecting the vehicles in the traffic jam conditions at sunset. The vehicles will cover the road surface so that the background information cannot be smoothly updated. Once the traffic is released, the existing background is not suitable for the moving segmentation. The main contribution of this paper is that an efficient vehicle detection approach is proposed to improve the detection accuracy in traffic jam conditions. The land mask decision gives the land information and the merged boundary box rule is presented to realize vehicle detection. The signed square normalized correlation coefficient calculation is addressed, and it is applied to vehicle tracking. The experimental results show that this approach works well in highway and urban area with high accuracy.
A new vehicle detection with distance estimation for lane change warning systems
(2007)
Wu, Bing-Fei
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Chen, Wei-Hsin
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Chang, Chai-Wei
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Chen, Chao-Jung
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Chung, Ming-Wei
In this paper, the vehicle detection with distance estimation algorithm is proposed by using two CCD cameras mounted on the both side mirrors of our experimental car, TAIWAN iTS-1. The Sobel edge pixels and gray intensity are applied for the lane marking and vehicle detection in our work. A new detection which utilizes a lane prediction technique to reduce the size of the ROI is developed for real-time embedded system applications. The side vehicles can be easily detected by comparing the gray intensity with the road surface. Then, the image coordinate model is utilized for the distance estimation with the detected approaching vehicles. The verified effective distance is 30m behind the host vehicle. This approach has been successfully implemented on the DSP board for the real-time detection and this system will be extended to the lane change warning system to give drivers alerts to avoid collision.
A Real-Time Driving Assistance and Surveillance System
(2009-09-01)
Chen, Chao-Jung
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Peng, Hsin-Yuan
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Wu, Bing-Fei
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Chen, Ying-Han
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電控工程研究所
Thousands of people are killed or injured in the vehicle accidents which are mostly caused by the carelessness of humans. Therefore, if there exists a warning system which can notify the driver of the approaching vehicles and the lane departure, the terrible tragedies can be greatly avoided. Moreover, if the driving status, such as the situation around the vehicle and the drivers' behaviors, can be recorded in videos, not only in texts, the police officers can reconstruct the scene easily. Furthermore, the increasing numbers of stolen cars is an important issue so the protection of the vehicles is essential. In this paper, a real-time DSP-based driving assistance and surveillance system is proposed. It can manipulate the images to detect the lane and the vehicles in both front and rear directions and the full-time driving status can also be recorded in H.264, After the car is parked, the monitor images inside the vehicle can be transmitted to users' mobile phones. The contribution of this paper is to provide an embedded system to improve the security at all-time, and the drivers, the passengers, and the vehicles can be much safer. The proposed system has been successfully implemented and fully tested by the real road environment in Taiwan so the stability and the reliability can be ensured.
A real-time multiple-vehicle detection and tracldng system with prior occlusion detection and resolution, and prior queue detection and resolution
(2006)
Lin, Shin-Ping
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Chen, Yuan-Hsin
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Wu, Bing-Fei
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電控工程研究所
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Institute of Electrical and Control Engineering
The proposed multiple-vehicle detection and tracking (MVDT) system utilizes a color background to segment moving objects and exploits relations among the moving objects and existed trajectories to track vehicles. Initially, the background is extracted by classification. Then, it is regularly updated by previous moving objects to guarantee robust segmentation in luminance-change circumstance. For partial wrong converged background due to roadside parking vehicles, it will be corrected later by checking fed back trajectories to avoid false detection after the vehicles moving away. In tracking processing, the relations of distances or distances and angles are applied to determine whether to create, extend, and delete a trajectory. If occlusion detected after trajectory creation, it will be resolved by rule-based tracking reasoning. Otherwise, lane information will be used. In addition, to further resolve occlusion occurred in lane, a queue detection and resolution technique will be utilized prior to tracking processing. Finally, for easy setup, parameter automation for the system is proposed.
A Real-Time Vision System for Nighttime Vehicle Detection and Traffic Surveillance
(2011-05-01)
Chen, Yen-Lin
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Wu, Bing-Fei
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Huang, Hao-Yu
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Fan, Chung-Jui
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電控工程研究所
This paper presents an effective traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic scenes. The proposed method identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis techniques. First, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied to effectively extract bright objects of interest. This automatic multilevel thresholding approach provides a robust and adaptable detection system that operates well under various nighttime illumination conditions. The extracted bright objects are then processed by a spatial clustering and tracking procedure that locates and analyzes the spatial and temporal features of vehicle light patterns, and identifies and classifies moving cars and motorbikes in traffic scenes. The proposed real-time vision system has also been implemented and evaluated on a TI DM642 DSP-based embedded platform. The system is set up on elevated platforms to perform traffic surveillance on real highways and urban roads. Experimental results demonstrate that the proposed traffic surveillance approach is feasible and effective for vehicle detection and identification in various nighttime environments.
A Real-Time Vision-Based Safety Assist System
(2008)
Wu, Bing-Fei
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Chen, Ying-Han
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Peng, Hsin-Yuan
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Chen, Chao-Jung
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電控工程研究所
In this paper, a real-time vision-based safety assist system is proposed to provide the driver an assistance system improving the security on driving and after parking. The estimated distances and a friendly intuitive graph are shown on the screen for drivers to examine the distance between other vehicles. The driving status can also be recorded in H.264. Furthermore, the users' mobile phones can monitor the images inside the vehicles to prevent the thieves after parking. The system has been implemented with the embedded systems and tested by the real road environment.
A Region of Interest Selection for Vision-Based Respiratory Rate Measurement in Sleeping Scenario
(2020-01-01)
Wu, Bing-Fei
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Lin, Chun-Han
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Huang, Po-Wei
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Chen, Kuan-Hung
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He, Da-Hong
Vision-based respiratory measurement can remotely measure respiratory information without affecting the sleep quality of the user. However, several scenarios during sleeping, such as when blankets cover areas of the face or body, can be a challenge when estimate respiratory rate (RR) due to the uncertainty of the region of interest (ROI). In this study, we first investigate the metrics and physical meanings of ROI selection from the perspectives of array signal processing and the concept of linear combining. Then, we propose an ROI detection algorithm based on both temporal and spatial consistency (TSC), which aims to extract the representative characteristics of respiration with fewer computational resources. Furthermore, a sleeping database, containing more than 50 hours of data, was built to investigate the performance of benchmarked ROI methods during long-term sleeping. The experimental results demonstrate that TSC attains the highest signal-to-noise ratio (SNR) at 20.6 dB and a relatively low elapsed time of 100.2 frames per second (fps). For the RR estimation accuracy, TSC reduces the mean absolute error from 1.65 breaths per minute (bpm) to 0.97 bpm.
A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking
(2014-08-01)
Wu, Bing-Fei
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Kao, Chih-Chung
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Jen, Cheng-Lung
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Li, Yen-Feng
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Chen, Ying-Han
This paper presents a relative discriminative histogram of oriented gradients (HOG) (RDHOG)-based particle filter (RDHOGPF) approach to traffic surveillance with occlusion handling. Based on the conventional HOG, an extension known as RDHOG is proposed, which enhances the descriptive ability of the central block and the surrounding blocks. RDHOGPF can be used to predict and update the positions of vehicles in continuous video sequences. RDHOG was integrated with the particle filter framework in order to improve the tracking robustness and accuracy. To resolve multiobject tracking problems, a partial occlusion handling approach is addressed, based on the reduction of the particle weights within the occluded region. Using the proposed procedure, the predicted trajectory is closer to that of the real rigid body. The proposed RDHOGPF can determine the target by using the feature descriptor correctly, and it overcomes the drift problem by updating in low-contrast and very bright situations. An empirical evaluation is performed inside a tunnel and on a real road. The test videos include low viewing angles in the tunnel, low-contrast and bright situations, and partial and full occlusions. The experimental results demonstrate that the detection ratio and precision of RDHOGPF both exceed 90%.
A variable step-size sign algorithm for channel estimation
(2014-09-01)
Li, Yuan-Ping
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Lee, Ta-Sung
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Wu, Bing-Fei
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
This paper proposes a new variable step-size sign algorithm (VSSA) for unknown channel estimation or system identification, and applies this algorithm to an environment containing two-component Gaussian mixture observation noise. The step size is adjusted using the gradient-based weighted average of the sign algorithm. The proposed scheme exhibits a fast convergence rate and low misadjustment error, and provides robustness in environments with heavy-tailed impulsive interference. (C) 2014 Elsevier B.V. All rights reserved.
A vision-based blind spot warning system for daytime and nighttime driver assistance
(2013-04-01)
Wu, Bing-Fei
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Huang, Hao-Yu
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Chen, Chao-Jung
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Chen, Ying-Han
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Chang, Chia-Wei
This paper proposes an effective blind spot warning system (BSWS) for daytime and nighttime conditions. The proposed BSWS includes camera models of a dynamic calibration and blind spot detection (BSD) algorithms for the daytime and nighttime. Under daytime conditions, the proposed system presents the Horizontal Edge and Shadow Composite Region (HESCR) method to extract the searching region and to acquire the shadow location of the targeted vehicles. Additionally, to detect vehicles at nighttime road scenes, the proposed system extracts bright objects and recognizes the paired headlights of the targeted vehicles for the BSD. The BSWS is implemented on a DSP-based embedded platform. The results of the BSWS are obtained by conducting practical experiments on our camera-assisted car on a highway in Taiwan under both nighttime and daytime conditions. Experimental results show that the proposed BSWS is feasible for vehicle detection and collision warning in various daytime and nighttime road environments. (C) 2013 Elsevier Ltd. All rights reserved.
A Vision-Based Collision Warning System by Surrounding Vehicles Detection
(2012-04-25)
Wu, Bing-Fei
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Chen, Ying-Han
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Kao, Chih-Chun
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Li, Yen-Feng
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Chen, Chao-Jung
To provide active notification and enhance drivers' awareness of their surroundings, a vision-based collision warning system that detects and monitors surrounding vehicles is proposed in this paper. The main objective is to prevent possible vehicle collisions by monitoring the status of surrounding vehicles, including the distance to the other vehicles in front, behind, to the left and to the right sides. In addition, the proposed system collects and integrates this information to provide advisory warnings to drivers. To offer the correct notification, an algorithm based on features of edge and morphology to detect vehicles is also presented. The proposed system has been implemented in embedded systems and evaluated on real roads in various lighting and weather conditions. The experimental results indicate that the vehicle detection ratios were higher than 97% in the daytime, and appropriate for real road applications.
Absolute Stability Analysis in Uncertain Static Fuzzy Control Systems with the Parametric Robust Popov Criterion
(2008)
Wu, Bing-Fei
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Ma, Li-Shan
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Perng, Jau-Woei
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Chin, Hung-I
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電控工程研究所
This study analyzes the absolute stability in static fuzzy logic control systems with certain and uncertain parameters. For certain static fuzzy control systems, the absolute stability can be analyzed with Popov criterion. The uncertain parameters for absolute stability analysis include the reference input, actuator gain and interval linear plant. The parametric robust Popov criterion based on Lur'e systems is applied to stability analysis respect to uncertain parameters. In our work, the parametric robust Popov criterion is applied to analyze absolute stability in static fuzzy logic control systems first time. This study can provide a valuable reference in designing fuzzy control systems. Finally, numerical simulations are provided to verify the analytical results.
Accompanist Recognition and Tracking for Intelligent Wheelchairs
(2014)
Wu, Bing-Fei
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Jen, Cheng-Lung
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Tsou, Tai-Yu
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Chen, Po-Yen
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電控工程研究所
Recently, several robotic wheelchairs have been proposed that employ autonomous functions. In designing wheelchairs, it is important to reduce the accompanist load. To provide such a task, the mobile robot needs to recognize and track people. In this paper, we propose to utilize the multisensory data fusion to track a target accompanist. First, the simultaneous localization and map building is achieved by using the laser range finder (LRF) and inertial sensors with the extended Kalman filter recursively. To track the target person robustly, the accompanist, are tracked by fusing laser and vision data. The human objects are detected by LRF, and the identity of accompanist is recognized using a PTZ camera with a pre-defined signature using the speed-up robust features algorithm. The proposed system can adaptively search visual signature and track the accompanist by dynamically zooming the PTZ camera based on LRF detection results to enlarge the range of human following. The experimental results verified and demonstrated the performance of the proposed system.
ACTIVE APPEARANCE MODEL ALGORITHM WITH K-NEAREST NEIGHBOR CLASSIFIER FOR FACE POSE ESTIMATION
(2014-06-01)
Wu, Bing-Fei
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Kao, Chih-Chung
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Jen, Cheng-Lung
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Chiang, Chia-Rong
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Lai, Po-Hung
In this paper, a face pose estimation (FPE) algorithm using active appearance model (AAM) with a k-nearest neighbor (kNN) classifier is presented. AAM is a model-based image representation method used to describe non-rigid visual objects, with both shape and texture variations, using a mean vector and linear combinations of a set of variation modes. Since AAM is a deformable model, it has several variations. Owing to the variations, the model is adjusted to the input test face image using iterative searching and fitting. The error, which measures the difference between the model and a test image, is minimized with the proposed searching algorithm. The face pose is then estimated using the distances between the landmark points in the AAM model with a INN classifier. Experimental results demonstrate that the proposed FPE algorithm can fit the face location with different face poses, with or without a hat, even wearing glasses, and identify the face pose accurately.