• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.540-543
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• 1999

We propose a face tracking algorithm using skin-color based segmentation and a robust Hausdorff distance. First, we present L＊a＊b＊ color model and face segmentation algorithm. A face is segmented from the first frame of input video sequences using skin-color map. Then, we obtain an initial face model with Laplacian operator. For tracking, a robust Hausdorff distance is computed and the best possible displacement t. is selected. Finally, the previous face model is updated using the displacement t. It is robust to some noises and outliers. We provide an example to illustrate the proposed tracking algorithm in video sequences obtained from CCD camera.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.283-299
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• 2014

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.711-718
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• 2003

In this paper, we propose a robust face tracking algorithm. It is based on Condensation algorithm [7] and uses skin color and facial shape as the observation measure. It is hard to integrate color weight and shape weight. So we propose the method that has two separate trackers which uses skin color and facial shape as the observation measure respectively. One tracker tracks skin colored region and the other tracks facial shape. We used importance sampling technique to limit sampling region of two trackers. For skin-colored region tracker, we propose an adaptive color model to avoid the effect of illumination change. The proposed face tracker performs robustly in clutter background and in the illumination changes.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.632-635
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• 2003

This paper describes a system fur tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.87-92
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• 2003

This paper describes a system for tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.182-192
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• 2017

In this paper, we propose hand tracking system combined with color and motion information. Most of hand detection and tracking systems are performed by modeling skin color. However, in this approach, since it is highly influenced by light or surrounding objects, accurate values cannot be derived constantly. Also, depending on the skin color, hand tracking may be interrupted by not only the hand but also the background with a color similar to that of the face and skin. Therefore, we design the hand tracking that can effectively track a hand by using motion history image(MHI) and combining it with CamShift. The proposed system is implemented based on C/C++, and the experiments proved that the proposed method shows stable and excellent performance.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.3-100
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• 2002

This paper presents the method for detection and tracking of multiple humans robustly in mobile platform. The perception of human is performed in real time through the processing of images acquired from a moving stereo vision system. We performed multi-cue integration such as human shape, skin color and depth information to detect and track each human in moving background scene. Human shape is measured by edge-based template matching on distance transformed image. Improving robustness for human detection, we apply the human face skin color in HSV color space. And we could increase the accuracy and the robustness in both detection and tracking by applying random sampling stochastic estimati...

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.283-294
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• 2006

In this paper, we propose a real-time face detection/tracking methodology with Haar wavelets and skin color. The proposed method boosts face detection and face tracking performance by combining skin color and Haar wavelets in an efficient way. The proposed method resolves the problem such as rotation and occlusion due to the characteristic of the condensation algorithm based on sampling despite it uses same features in both detection and tracking. In particular, it can be applied to a variety of applications such as face recognition and facial expression recognition which need an exact position and size of face since it not only keeps track of the position of a face, but also covers the size variation. Our test results show that our method performs well even in a complex background, a scene with varying face orientation and so on.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.347-350
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• 2005

This paper proposes a new tracking algorithm using the Kalman estimator based color SSD algorithm. The Kalman estimator includes the color information as well as the position and size of the face region in its state vector, to take care of the variation of skin color while faces are moving. Based on the estimated face position, the color SSD algorithm finds the face matching with the one in the previous frame even when the color and size of the face region vary. The features of a face region extracted by the color SSD algorithm are used to update the state of the Kalman estimator.