• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.112-118
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• 2014

This paper deals with a color-based tracking of a moving object. Firstly, existing Camshift algorithm is complemented to improve the tracking weakness in the brightness change of an image which occurs in every frame. The complemented Camshift still shows unstable tracking when the objects with same color of the tracking object exist in background. In order to overcome the drawback this paper proposes the Camshift combined with KLT algorithm based on optical flow. The KLT algorithm performing the pixel-based feature tracking can complement the shortcoming of Camshift. Experimental results show that the merged tracking method makes up for the drawback of the Camshit algorithm and also improves tracking performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.827-833
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• 2012

In this paper, we propose an improved optical flow algorithm which reduces computational complexity as well as noise level. This algorithm reduces computational time by applying level simplification technique and removes noise by using eigenvectors of objects. Optical flow is one of the accurate algorithms used to generate depth information from two image frames using the vectors which track the motions of pixels. This technique, however, has disadvantage of taking very long computational time because of the pixel-based calculation and can cause some noise problems. The level simplifying technique is applied to reduce the computational time, and the noise is removed by applying optical flow only to the area of having eigenvector, then using the edge image to generate the depth information of background area. Three-dimensional images were created from two-dimensional images using the proposed method which generates the depth information first and then converts into three-dimensional image using the depth information and DIBR(Depth Image Based Rendering) technique. The error rate was obtained using the SSIM(Structural SIMilarity index).

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.6
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• pp.10-21
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• 2000

The Cellular Noninear Networks structure for Distance Transform(DT) and the robust optical flow detection algorithm based on the DT are proposed. For some applications of optical flows such as target tracking and camera ego-motion computation, correct optical flows at a few feature points are more useful than unreliable one at every pixel point. The proposed algorithm is for detecting the optical flows on the trajectories only of the feature points. The translation lengths and the directions of feature movements are detected on the trajectories of feature points on which Distance Transform Field is developed. The robustness caused from the use of the Distance Transform and the easiness of hardware implementation with local analog circuits are the properties of the proposed structure. To verify the performance of the proposed structure and the algorithm, simulation has been done about various images under different noisy environment.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1154-1166
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• 1991

Optical flow is important not only for determining velocity and trajectory of the object but also for image segmentation and three dimensional information. In this paper an algorithm for segmentation of the optical flow field is presented. This method is based on a pyramid linking method combined with the proposed models. In this method each node contained a model of the flow in the region that it represented regions were combined by taking the model that best fit the union of the two regions. Each node linked to one of its fathers based on the error between the pixels represented by the node and the father's model of its flow. A major problem which has emerged in conventional researchs on optical flow field is sensitive to noise the proposed method is relatively insensitive to noise at the result of computer simulation the pyramid algorithm proposed in this paper seem to have useful properties.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.129-135
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• 2011

In this paper, we propose the algorithm using optical flow and machine learning-based segmentation for the 3D conversion of 2D video. For the segmentation allowing the successful 3D conversion, we design a new energy function, where color/texture features are included through machine learning method and the optical flow is also introduced in order to focus on the regions with the motion. The depth map are then calculated according to the optical flow of segmented regions, and left/right images for the 3D conversion are produced. Experiment on various video shows that the proposed method yields the reliable segmentation result and depth map for the 3D conversion of 2D video.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.91-98
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• 2008

This paper describes the recognition method of moving objects in mobile robot with an omnidirectional camera. The moving object is detected using the specific pattern of an optical flow in omnidirectional image. This paper consists of two parts. In the first part, the pattern of an optical flow is investigated in omnidirectional image. The optical flow in omnidirectional image is influenced on the geometry characteristic of an omnidirectional camera. The pattern of an optical flow is theoretically and experimentally investigated. In the second part, the detection of moving objects is presented from the estimated optical flow. The moving object is extracted through the relative evaluation of optical flows which is derived from the pattern of optical flow. In particular, Focus-Of-Expansion (FOE) and Focus-Of-Contraction (FOC) vectors are defined from the estimated optical flow. They are used as reference vectors for the relative evaluation of optical flows. The proposed algorithm is performed in four motions of a mobile robot such as straight forward, left turn, right turn and rotation. Experimental results using real movie show the effectiveness of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.95-100
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• 2013

Localization is very important for the autonomous navigation of Unmanned Ground Vehicles; however, it is difficult that they have a precise Inertial Navigation System(INS) sensor, especially Small Unmanned Ground Vehicle(SUGV). Moreover, there are some condition such as denial of global position system(GPS), GPS/INS integrated system is not robust. This paper proposes the estimation algorithm with optical flow sensor and INS. Being compared with previous researches, the proposed algorithm is suitable for skid steering vehicles. We revised the measurement model of previous research for the accuracy of side direction position. Experimental results were performed to verify the algorithm, and the result showed an excellent performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.215-216
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• 2020

Optical Flow는 컴퓨터 비전 분야의 많은 응용기술에 사용된다. 객체 탐지, 추적, 연속 영상 보간, 3D Reconstruction과 같은 최근에 활발히 연구되는 여러 분야에서 사용되는 기반 기술이다. 최근 딥러닝을 기반으로 한 다양한 연구가 활발히 진행되어 왔으며 높은 정확도를 보이고 있다. 이런 분야들은 많은 경우에 실시간 시스템에 적용되어 이미지로부터 정보를 연산한다. 본 논문은 MaskFlownet, SelFlow, LiteFlowNet2 등과 같은 높은 정확도를 가진 신경망 네트워크로 추정된 Optical Flow를 살펴본다. 각 신경망 네트워크로 얻어진 정확도를 비교하고 디스플레이 기술과 이미지 센서 기술의 발전으로 사용 수요가 많아진 고화질의 이미지를 실시간으로 처리하는 경우, 적용 가능한 Optical Flow의 성능을 분석하였다.