Ho Gi Jung

A New Approach to Urban Pedestrian Detection for Automatic Braking

This paper presents an application of a pedestrian-detection system aimed at localizing potentially dangerous situations under specific urban scenarios. The approach used in this paper differs from those implemented in traditional pedestrian-detection systems, which are designed to localize all pedestrians in the area in front of the vehicle. Conversely, this approach searches for pedestrians in critical areas only. The environment is reconstructed with a standard laser scanner, whereas the following check for the presence of pedestrians is performed due to the fusion with a vision system. The great advantages of such an approach are that pedestrian recognition is performed on limited image areas, therefore boosting its timewise performance, and no assessment on the danger level is finally required before providing the result to either the driver or an onboard computer for automatic maneuvers. A further advantage is the drastic reduction of false alarms, making this system robust enough to control nonreversible safety systems.

Parking Slot Markings Recognition for Automatic Parking Assist System

This paper describes a monocular vision based parking-slot-markings recognition algorithm, which is used to automate the target position selection of automatic parking assist system. Peak-pair detection and clustering in Hough space recognize marking lines. Specially, one-dimensional filter in Hough space is designed to utilize a priori knowledge about the characteristics of marking lines in birds eye view edge image. Modified distance between point and line-segment is used to distinguish guideline from recognized marking line-segments. Once the guideline is successfully recognized, T-shape template matching easily recognizes dividing marking line-segments. Experiments show that proposed algorithm successfully recognizes parking slots even when adjacent vehicles occlude parking-slot-markings severely

Real-Time Gaze Estimator Based on Driver's Head Orientation for Forward Collision Warning System

This paper presents a vision-based real-time gaze zone estimator based on a drivers head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a drivers yaw. Third, we propose new features-the normalized mean and the standard deviation of the horizontal edge projection histogram-to reliably and rapidly estimate a drivers pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200 000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.

Scanning Laser Radar-Based Target Position Designation for Parking Aid System

Recently, customers have shown a growing interest in parking aid products. A parking aid system consists of target position designation, path planning, and parking guidance by user interface or path tracking by active steering. For target position designation, various sensors and signal processing technologies have been tried. In the case of parallel parking, an ultrasonic sensor-based method plays a dominant role. In the case of perpendicular parking, a graphical user interface (GUI)-based method and a parking slot marking-based method have been commercialized. However, methods developed for the recognition of free parking space between vehicles have their respective drawbacks. This paper proposes a method for the recognition of free parking space between vehicles using scanning laser radar. This proposed method consists of range data preprocessing, corner detection, and target parking position designation. The authors developed a novel corner-detection method consisting of rectangular corner detection and round corner detection. The newly developed corner detection is unaffected by cluster orientation and the range data interval and is robust to noise. Experimental results showed that even in situations where other methods failed, the proposed scanning laser radar-based method could designate target parking position to viable free parking space. The recognition rate was 98.21%, and the average processing time was about 600 ms. Finally, it is argued that the proposed method will eventually be a practical solution because of the decreasing price of scanning laser radar and multiple-function integration strategies.

Sensor Fusion-Based Vacant Parking Slot Detection and Tracking

This paper proposes a vacant parking slot detection and tracking system that fuses the sensors of an Around View Monitor (AVM) system and an ultrasonic sensor-based automatic parking system. The proposed system consists of three stages: parking slot marking detection, parking slot occupancy classification, and parking slot marking tracking. The parking slot marking detection stage recognizes various types of parking slot markings using AVM image sequences. It detects parking slots in individual AVM images by exploiting a hierarchical tree structure of parking slot markings and combines sequential detection results. The parking slot occupancy classification stage identifies vacancies of detected parking slots using ultrasonic sensor data. Parking slot occupancy is probabilistically calculated by treating each parking slot region as a single cell of the occupancy grid. The parking slot marking tracking stage continuously estimates the position of the selected parking slot while the ego-vehicle is moving into it. During tracking, AVM images and motion sensor-based odometry are fused together in the chamfer score level to achieve robustness against inevitable occlusions caused by the ego-vehicle. In the experiments, it is shown that the proposed method can recognize the positions and occupancies of various types of parking slot markings and stably track them under practical situations in a real-time manner. The proposed system is expected to help drivers conveniently select one of the available parking slots and support the parking control system by continuously updating the designated target positions.

Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new driver-monitoring method considering both factors. We make the following contributions. First, if the driver is looking ahead, drowsiness detection is performed; otherwise, distraction detection is performed. Thus, the computational cost and eye-detection error can be reduced. Second, we propose a new eye-detection algorithm that combines adaptive boosting, adaptive template matching, and blob detection with eye validation, thereby reducing the eye-detection error and processing time significantly, which is hardly achievable using a single method. Third, to enhance eye-detection accuracy, eye validation is applied after initial eye detection, using a support vector machine based on appearance features obtained by principal component analysis (PCA) and linear discriminant analysis (LDA). Fourth, we propose a novel eye state-detection algorithm that combines appearance features obtained using PCA and LDA, with statistical features such as the sparseness and kurtosis of the histogram from the horizontal edge image of the eye. Experimental results showed that the detection accuracies of the eye region and eye states were 99 and 97%, respectively. Both driver drowsiness and distraction were detected with a success rate of 98%.

Automatic free parking space detection by using motion stereo-based 3D reconstruction

This paper proposes a free parking space detection system by using motion stereo-based 3D reconstruction. An image sequence is acquired with a single rearview fisheye camera and the view behind the automobile is three-dimensionally reconstructed by using point correspondences. Metric information is recovered from the camera height ratio and free parking spaces are detected by estimating the positions of adjacent vehicles. Since adjacent vehicles are usually located near the epipole, their structures are seriously degraded. To solve this problem, we select point correspondences by using a de-rotation-based method and mosaic 3D structures by estimating a similarity transformation. Unlike in previous work, our system proposes an efficient way of locating free parking spaces in 3D point clouds. Odometry is not used because its accuracy depends largely on road conditions. In the experiments, the system was tested in 154 different parking situations and its success rate was 90% (139 successes in 154 cases). The detection accuracy was evaluated by using ground truth data that was acquired with a laser scanner.

Mixture of Gaussians-Based Background Subtraction for Bayer-Pattern Image Sequences

This letter proposes a background subtraction method for Bayer-pattern image sequences. The proposed method models the background in a Bayer-pattern domain using a mixture of Gaussians (MoG) and classifies the foreground in an interpolated red, green, and blue (RGB) domain. This method can achieve almost the same accuracy as MoG using RGB color images while maintaining computational resources (time and memory) similar to MoG using grayscale images. Experimental results show that the proposed method is a good solution to obtain high accuracy and low resource requirements simultaneously. This improvement is important for a low-level task like background subtraction since its accuracy affects the performance of high-level tasks, and is preferable for implementation in real-time embedded systems such as smart cameras.

Localization and analysis of critical areas in urban scenarios

This paper presents an application of a pedestrian detection system aimed at localizing potentially dangerous situations in specific urban scenarios. The approach used in this work differs from the one implemented in traditional pedestrian detection systems, which are designed to localize all pedestrians appearing in the area in front of the vehicle. This application first locates critical areas in the urban environment, and then it searches for pedestrians in these areas only. The environment is reconstructed with a standard laser scanner system, while the following check for the presence of pedestrians is performed thanks to the fusion with a vision system. The great advantages of such an approach are that pedestrian recognition is performed on a very limited image area -therefore boosting its timing performance- and no assessment on the danger level is finally required before providing the result to either the driver or an on-board computer for automatic manoeuvres.

Non-intrusive Iris Image Capturing System Using Light Stripe Projection and Pan-Tilt-Zoom Camera

This paper proposes non-intrusive iris image capturing system, which consists of pan-tilt-zoom camera and light stripe projection. Light stripe projection provides the position of user. After panning according to users position, AdaBoost-based face detection finds tilt angle. With users position and tilt angle, zoom and focus position are initialized. Users position replaces 2D face search with ID face search. Exact zoom and focus position enable fast control and narrow search range. Consequently, experimental results show that proposed system can capture iris image within acceptable time.