• Journal of Digital Convergence
• /
• v.13 no.12
• /
• pp.209-215
• /
• 2015

In the past, there have been many theory and algorithms for vehicle tracking. But the time complexity of many feature point matching methods for vehicle tracking are exponential. Also, object segmentation and detection algorithms presented for vehicle tracking are exhaustive and time consuming. Therefore, we present the fast and efficient two stages method that can efficiently track the many moving vehicles on the road. The first detects the vehicle plate regions and extracts the feature points of vehicle plates. The second associates the feature points between frames using dynamic programming.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.8
• /
• pp.1802-1808
• /
• 2003

In this paper, we propose a method about the extraction of the pedestrian tracking trajectory in the road and we used the method of mosaic background extraction and adaptive neural network for automatic pedestrian tracking system. We used mosaic background extraction to overcome ghost phenomenon. And we detected pedestrian using differential image analysis. We used adaptive neural network for multiple pedestrian tracking that non­rigid form moving. The ART2 network is capable of detecting the mass­centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment show promising results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.04a
• /
• pp.500-504
• /
• 2002

The purpose of this paper is to implement a guidance system for an object on the road. A watch camera equipped on the auto door recognizes the direction for the destination of an object, after that it determines whether opening or closing the door, and then the door is opened automatically, based on the decision. The motion of the moving object is approximated by using the technique of the image processing of tracking images and the affine model. The direction of the moving object is predicted from image information obtained by applying linear Kalman filter to the motion estimation in order to reduce the search region, the moving position, and the direction of the center of the object. Along with the guidance function, the system has the announcing function to the object. The experimental results confirm the veridity and applicability of the proposed system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.115.5-115
• /
• 2002

In this paper, we introduce a system that detects all kinds of violations at a street intersection such as red light running, speed violation, stop line violation and lane violation by tracking individual vehicles. Two cameras are used for defecting violations. One is an analog camera for real-time tracking and the other is a digital camera for license plate reading. This system is connected to the traffic signal system controller and monitors the red, arrow, yellow and green phases of an approach. Two loops in the road are used to detect vehicle approach and speed. The system takes pictures of all vehicles passing a second loop and tracks the vehicles until they go out a street intersection...

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.10 no.3
• /
• pp.64-73
• /
• 2011

This paper presents an algorithm for tracking lanes on the road based on corner detection techniques. The proposed algorithm shows high accuracy regardless of lane divider types, eg, solid line, dashed line, etc, and thus is of advantage to city streets and local roads where various types of lane dividers are used. A set of experiments was conducted on real roads with various types of lane dividers and results show an extract ratio over 87% in average.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.2
• /
• pp.130-136
• /
• 2015

In this paper, we propose a robust curved lane marking detection method. Several lane detection methods have been proposed, however most of them have considered only straight lanes. Compared to the number of straight lane detection researches, less number of curved-lane detection researches has been investigated. This paper proposes a new curved lane detection and tracking method which is robust to various illumination conditions. First, the proposed methods detect straight lanes using a robust road feature image. Using the geometric relation between a vehicle camera and the road plane, several circle models are generated, which are later projected as curved lane models on the camera images. On the top of the detected straight lanes, the curved lane models are superimposed to match with the road feature image. Then, each curve model is voted based on the distribution of road features. Finally, the curve model with highest votes is selected as the true curve model. The performance and efficiency of the proposed algorithm are shown in experimental results.

• 한국도로학회:학술대회논문집
• /
• 2004.10a
• /
• pp.103-110
• /
• 2004

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06a
• /
• pp.285-286
• /
• 2008

• 한국도로학회:학술대회논문집
• /
• 2003.10a
• /
• pp.35-40
• /
• 2003

• Spatial Information Research
• /
• v.21 no.3
• /
• pp.11-19
• /
• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.