• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.89-96
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• 2010

In this paper, we proposed a method about improving face tracking efficiency of face detection for AF system using the faces to the ROI. The conventional face detection system detecting faces based skin color uses the ratio of skin pixels of the present frame to detected face regions of the past frame to track the faces. The tracking method is superior in the stability of the regions but it is inferior in the face tracking efficiency. We proposed a face tracking method using the area of the overlapping region in the detected face regions of the past frame and the present frame to improve the tracking efficiency. The proposed face tracking efficiency demonstration was performed by making a film of face detection with face tracking in real-time and using the moving traces of the detected faces.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.908-928
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• 2022

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.285-288
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• 2017

In this paper, we propose a vision-based object detection and tracking system using online learning. The proposed system adopts a feature point-based method for tracking a series of inter-frame movement of a newly detected object, to estimate rapidly and toughness. At the same time, it trains the detector for the object being tracked online. Temporarily using the result of the failure detector to the object, it initializes the tracker back tracks to enable the robust tracking. In particular, it reduced the processing time by improving the method of updating the appearance models of the objects to increase the tracking performance of the system. Using a data set obtained in a variety of settings, we evaluate the performance of the proposed system in terms of processing time.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.1-8
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• 2022

In this paper, we propose a fire detection system based on CCTV images using an object tracking technology with YOLOv4 model capable of real-time object detection and a DeepSORT algorithm. The fire detection model was learned from 10800 pieces of learning data and verified through 1,000 separate test sets. Subsequently, the fire detection rate in a single image and fire detection maintenance performance in the image were increased by tracking the detected fire area through the DeepSORT algorithm. It is verified that a fire detection rate for one frame in video data or single image could be detected in real time within 0.1 second. In this paper, our AI fire detection system is more stable and faster than the existing fire accident detection system.

• Journal of Advanced Navigation Technology
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• v.6 no.3
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• pp.231-240
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• 2002

For a long time target maneuvers in tracking problem have been a difficult task to handle. In order to solve this problems. there have been various tracking techniques. In the development of a tracking filter for a maneuvering target, maneuver detection threshold plays a key role. However, no study of filter performance the varying maneuver detection threshold has been carried out so far. Instead, the maneuver detection have been chosen empirically. In this paper, the effect of detection threshold selection on the performance of the tracking filters was considered and the relationships between maneuvers and the detection threshold have been analyzed by simulation.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.168-172
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• 2014

In this paper, we propose an algorithm that can efficiently detect and monitor multiple ships in real-time. The proposed algorithm uses morphological operations and edge information for detecting and tracking ships. We used smoothing filter with a $3{\times}3$ Gaussian window and luminance component instead of RGB components in the captured image. Additionally, we applied Sobel operator for edge detection and a threshold for binary images. Finally, object labeling with connectivity and morphological operation with open and erosion were used for ship detection. Compared with conventional methods, the proposed method is meant to be used mainly in coastal surveillance systems and monitoring systems of harbors. A system based on this method was tested for both stationary and non-stationary backgrounds, and the results of the detection and tracking rates were more than 97% on average. Thousands of image frames and 20 different video sequences in both online and offline modes were tested, and an overall detection rate of 97.6% was achieved.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.177-186
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• 2008

Video Image Detection System can be used for various traffic managements including traffic operation and traffic safety. Video Image Detection Technique can be divide by Tripwire System and Tracking System. Autoscope, which is widely used in the market, utilizes the Tripwire System. In this study, we developed an individual vehicle tracking system that can collect microscopic traffic information and also developed another image detection technology under the Tripwire System. To prove the accuracy and reliability of the newly developed systems, we compared the traffic data of the systems with those generated by Autoscope. The results showed that 0.35% of errors compared with the real traffic counts and 1.78% of errors with Autoscope. Performance comparisons on speed from the two systems showed the maximum errors of 1.77% with Autoscope, which confirms the usefulness of the newly developed systems.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2962-2964
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• 1999

We know the traffic information about the velocity and position of vehicle by extraction and tracking vehicle from continuosly obtained road image of camera. The conventional method of vehicle detection indicate increment of error due to headlight and taillight in night road image. This paper show such as vehicle detection of binary, Edge detection. amalgamation of image are applied to extract the vehicle, and Kalman filter is adaptive methods for tracking position and velocity of vehicle.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.2
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• pp.15-19
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• 2008

In this paper, we propose a new algorithm of image depth detection using motion estimation and object tracking. In industry, robots are used for automobile, conveyer system, etc. But, these have much necessary time. Thus, in this paper, we develop the efficient method of image depth detection based on motion estimation and object tracking.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.40-41
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• 2023

This paper introduces the design of detection, recognition, and tracking algorithms for VTS radar image-based objects. The detection of objects in radar images utilizes artificial intelligence technology to determine the presence or absence of objects, and can classify the type of object using AI technology. Tracking involves the continuous tracking of detected objects over time, including technology to prevent confusion in the movement path. In particular, for land-based radar, there are unnecessary areas for detection depending on the terrain, so the function of detecting and recognizing vessels within the region of interest (ROI) set in the radar image is included. In addition, the extracted coordinate information is designed to enable various applications and interpretations by calculating speed, direction, etc.