• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.137-139
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• 2011

영상에서의 경계선 추출은 원 영상의 노이즈에 의해 크게 영향을 받는다. 따라서 먼저 그 노이즈들을 제거할만한 어떤 방법들이 필요하다. Mean Shift 알고리즘은 이러한 목적에 부합되는 유연한 함수로서, 별로 중요하지 않은 정보와 민감한 노이즈 부분을 점점 제거하는데 알맞다. 여기서는 Canny 알고리즘을 사용하여 중점으로 하는 영상에서의 윤곽선을 찾아낸다. 그리고 테스트 하고 이전의 유일한 Canny 알고리즘 보다 결과가 좋음을 알아낸다.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.77-83
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• 2019

In this paper, we propose an object tracking algorithm based on color histogram and convolutional neural network model. In order to increase the tracking accuracy, we synthesize generic object tracking using regression network algorithm which is one of the convolutional neural network model-based tracking algorithms and a mean-shift tracking algorithm which is a color histogram-based algorithm. Both algorithms are classified through support vector machine and designed to select an algorithm with higher tracking accuracy. The mean-shift tracking algorithm tends to move the bounding box to a large range when the object tracking fails, thus we improve the accuracy by limiting the movement distance of the bounding box. Also, we improve the performance by initializing the tracking start positions of the two algorithms based on the average brightness and the histogram similarity. As a result, the overall accuracy of the proposed algorithm is 1.6% better than the existing generic object tracking using regression network algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.213-218
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• 2013

In this paper, a video based ship detection method is proposed to monitor port efficiently. Visual saliency map algorithm and mean shift algorithm is applied to detect moving ships don't include background information which is difficult to track moving ships. It is easy to detect ships at the port using saliency map algorithm, because it is very effective to extract saliency object from background. To remove background information in the saliency region, image segmentation and clustering using mean shift algorithm is used. As results of detecting simulation with images of a camera installed at the harbor, it is shown that the proposed method is effective to detect ships.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2417-2422
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• 2015

Mean-Shift tracking using hue has a problem that it fail in the object tracking when background has similar hue to the object. This paper proposes an improved Mean-Shift tracking algorithm using new data instead of a hue. The new data is generated by adaptive mixture of hue and saturation which have low interrelationship . That is, the proposed algorithm selects a main attribute of color that is able to distinguish the object and background well and a secondary one which don't, and places their upper 4 bits on upper 4 bits and lower 4 bits on the mixture data, respectively. The proposed algorithm properly tracks the object, keeping tracking error maximum 2.0~4.2 pixel and average 0.49~1.82 pixel, by selecting the saturation as the main attribute of color under tracking environment that background has similar hue to the object.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.421-427
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• 2011

The importance of the real-time object recognition and tracking field has been growing steadily due to rapid advancement in the computer vision applications industry. As is well known, the mean-shift algorithm is widely used in robust real-time object tracking systems. Since the mentioned algorithm is easy to implement and efficient in object tracking computation, many say it is suitable to be applied to real-time object tracking systems. However, one of the major drawbacks of this algorithm is that it always converges to a local mode, failing to perform well in a cluttered environment. In this paper, an Optical Flow-based algorithm which fits for real-time recognition of multiple moving objects is proposed. Also in the tests, the newly proposed method contributed to raising the similarity of multiple moving objects, the similarity was as high as 0.96, up 13.4% over that of the mean-shift algorithm. Meanwhile, the level of pixel errors from using the new method keenly decreased by more than 50% over that from applying the mean-shift algorithm. If the data processing speed in the video surveillance systems can be reduced further, owing to improved algorithms for faster moving object recognition and tracking functions, we will be able to expect much more efficient intelligent systems in this industrial arena.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.136-141
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• 2009

This paper proposed a framework for handover method in continuously tracking a person of interest across cooperative pan-tilt-zoom (PTZ) cameras. The algorithm here is based on a robust non-parametric technique for climbing density gradients to find the peak of probability distributions called the mean shift algorithm. Most tracking algorithms use only one cue (such as color). The color features are not always discriminative enough for target localization because illumination or viewpoints tend to change. Moreover the background may be of a color similar to that of the target. In our proposed system, the continuous person tracking across cooperative PTZ cameras by mean shift tracking that using color and shape histogram to be feature distributions. Color and shape distributions of interested person are used to register the target person across cameras. For the first camera, we select interested person for tracking using skin color, cloth color and boundary of body. To handover tracking process between two cameras, the second camera receives color and shape cues of a target person from the first camera and using linear color calibration to help with handover process. Our experimental results demonstrate color and shape feature in mean shift algorithm is capable for continuously and accurately track the target person across cameras.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2067-2078
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• 2017

In this paper, a new face tracking algorithm is proposed. The CamShift (Continuously adaptive mean SHIFT) algorithm shows unstable tracking when there exist objects with similar color to that of face in the background. This drawback of the CamShift is resolved by the proposed algorithm using Kinect's pixel-by-pixel depth information and the skin detection method to extract candidate skin regions in HSV color space. Additionally, even when the target face is disappeared, or occluded, the proposed algorithm makes it robust to this occlusion by the feature point matching. Through experimental results, it is shown that the proposed algorithm is superior in tracking performance to that of existing TLD (Tracking-Learning-Detection) algorithm, and offers faster processing speed. Also, it overcomes all the existing shortfalls of CamShift with almost comparable processing time.

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.937-944
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• 2007

In this paper, we propose multiple human tracking with an moving stereo camera. The tracking process is based on mean shift algorithm which is using color information of the target. Color based tracking approach is invariant to translation and rotation of the target but, it has several problems. Because of mean shift uses color distribution, it is sensitive to color distribution of background and targets. In order to solve this problem, we combine color and depth information of target. Also, we build human body part model to handle occlusions and we have created adaptive box scale. As a result, the proposed method is simple and efficient to track multiple humans in real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1175-1183
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• 2004

This paper propose a method to trace a moving object based on the information directly obtained from MPEG-2 compressed video stream without decoding process. In the proposed method, the motion flow is constructed from the motion vectors involved in compressed video and then we calculate the amount of pan, tilt, zoom associated with camera operations using generalized Hough transform. The local object motion can be extracted from the motion flow after the compensation with the parameters related to the global camera motion. The moving object is designated initially by a user via bounding box. After then automatic tracking is performed based on the mean-shift algorithm of the motion flows of the object. The proposed method can improve the computation speed because the information is directly obtained from the MPEG-2 compressed video, but the object boundary is limited by blocks rather than pixels.

• Transactions on Electrical and Electronic Materials
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• v.13 no.1
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• pp.10-15
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• 2012

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.