Browsing by Author "Li, Yen-Feng"

Now showing 1 - 3 of 3

A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking
(2014-08-01)
Wu, Bing-Fei
;
Kao, Chih-Chung
;
Jen, Cheng-Lung
;
Li, Yen-Feng
;
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 Collision Warning System by Surrounding Vehicles Detection
(2012-04-25)
Wu, Bing-Fei
;
Chen, Ying-Han
;
Kao, Chih-Chun
;
Li, Yen-Feng
;
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.
Reasoning-Based Framework for Driving Safety Monitoring Using Driving Event Recognition
(2013-09-01)
Wu, Bing-Fei
;
Chen, Ying-Han
;
Yeh, Chung-Hsuan
;
Li, Yen-Feng
;
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.