Browsing by Author "Chen, Ying-Han"

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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 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 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 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.
Driving behaviour-based event data recorder
(2014-06-01)
Wu, Bing-Fei
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Chen, Ying-Han
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Yeh, Chung-Hsuan
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電機工程學系
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Department of Electrical and Computer Engineering
A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.
Fuzzy Logic Based Driving Behavior Monitoring using Hidden Markov Models
(2012-01-01)
Wu, Bing-Fei
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Chen, Ying-Han
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Yeh, Chung-Hsuan
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
This paper proposes a driving behavior monitoring system to provide drivers an indicator of the danger level for driving safety. The major challenge of the research is to label whether a pattern is dangerous or not for training data. Our approach is to recognize the seven behaviors, normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving, and approaching the car in front by hidden Markov models. The danger-level indicator, on behalf of the dangerous situation of drivers, is inferred by fuzzy rules involved with the above behaviors. The higher the value represents the worse status of drivers, and it also provides three colors representing different levels of warnings to remind drivers of their own states. The uncertain definition of a dangerous pattern is avoided, and the related behaviors leading to the current danger level are offered instead. Moreover, unlike many studies using simulators to validate their systems and experimental results, we collected data from a real vehicle and evaluated the proposed system in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behavior recognition and can be used for driving safety.
GPS TRACKING SYSTEM
(2010-12-23)
WU, BING-FEI
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Chen, Ying-Han
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Chen, Chao-Jung
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Kao, Chih-Chung
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Huang, Po-Chia
The present invention relates a GPS tracking system, comprising a tracking and positioning device, a server, a positioning display unit, and a database. Since the tracking and positioning device only sends an Internet protocol (IP) data and a verification data once when it is connected to the Internet, and sends a positioning data only when a position of the tracking and positioning device is to be shown on the positioning display unit, the bandwidth can be saved.
Intelligent driving assistant systems
(2010-03-17)
Wu, Bing-Fei
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Chen, Chao-Jung
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Peng, Hsin-Yuan
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Chen, Ying-Han
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Lin, Wen-Hsin
The invention discloses an intelligent driving assistant system applied to a handheld device. The invention can detect more than one safety mode including lane departure detection, lost-cargo detection, detecting front-object under driving condition, detecting side-object under driving condition and detecting back-object under driving condition, also the invention can mention alarm according to the detection results from different modules. Finally the invention can store the real-time image according to the detection results from different modules, and then transfer the related information to other places for real-time notice with matching the information from the GPS system and the digitized map.
Reasoning-Based Framework for Driving Safety Monitoring Using Driving Event Recognition
(2013-09-01)
Wu, Bing-Fei
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Chen, Ying-Han
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Yeh, Chung-Hsuan
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Li, Yen-Feng
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電機工程學系
With the growing concern for driving safety, many driving-assistance systems have been developed. In this paper, we develop a reasoning-based framework for the monitoring of driving safety. The main objective is to present drivers with an intuitively understood green/yellow/red indicator of their danger level. Because the danger levelmay change owing to the interaction of the host vehicle and the environment, the proposed framework involves two stages of danger-level alerts. The first stage collects lane bias, the distance to the front car, longitudinal and lateral accelerations, and speed data from sensors installed in a real vehicle. All data were recorded in a normal driving environment for the training of hidden Markov models of driving events, including normal driving, acceleration, deceleration, changing to the left or right lanes, zigzag driving, and approaching the car in front. In addition to recognizing these driving events, the degree of each event is estimated according to its character. In the second stage, the danger-level indicator, which warns the driver of a dangerous situation, is inferred by fuzzy logic rules that address the recognized driving events and their degrees. A hierarchical decision strategy is also designed to reduce the number of rules that are triggered. The proposed framework was successfully implemented on a TI DM3730-based embedded platform and was fully evaluated in a real road environment. The experimental results achieved a detection ratio of 99% for event recognition, compared with that achieved by four conventional methods.
The Blind Spot Warning System for Daytime and Nighttime
(2012)
Wu, Bing-Fei
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Huang, Hao-Yu
;
Chen, Chao-Jung
;
Chen, Ying-Han
;
Chang, Chia-Wei
This paper proposes an effective blind spot detection and warning system in the daytime and nighttime condition. In the daytime condition, 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 overcome the nighttime scene, the proposed system extracts the bright objects and recognizes the paired headlights of the targeted vehicles for the BSD. The results of the proposed system are yielded by the practical experiments on the highway in Taiwan at the nighttime and daytime conditions. Experimental results show that the proposed BSD system is feasible and effective for vehicle detection and identification in the daytime and nighttime environments.