Human Motion Capture System Using Color Markers and Silhouette

P E1 G H1 K1 F1
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E2 H2 K2 F2
Computer Human motion Video cameras
Fig. 1. Motion capture system.
Fig. 2. Feature points.
I. INTRODUCTION Motion capture[1] is a technique to measure a human motion and posture. This technique is usually used to make computer graphics of moving human in the field of entertainments such as movies, TV games and so on. Moreover, recently, the motion capture techniques are also used in the analysis of the motion of human body and the rehabilitation for the physically handicapped persons[2]. There are many systems for the motion capture such as the mechanical type[3], the magnetic type[4], optical type and so on. However, the mechanical and magnetic types need to equip various sensor devices on a human body, so the human motion is restricted. On the other hand, the optical type need to equip only simple color markers, and since this system measure the human motion from images taken by video cameras, it can acquire more natural motions than the mechanical and magnetic types. In case of this type, if we need to measure many positions of the human body, we should just prepare many different color
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Hue [deg.]
Fig. 4. Histogram of hue in lab room. Fig. 3. Overview of proposed system.
a computer and 2D feature positions on the images and 3D feature positions are estimated. Note that, in this system, the human motion images are recorded in the computer first and then they are processed. Figure 2 shows the measured feature points on the human body. The positions of elbows (E1 and E2 ) and knees (K1 and K2 ) are detected by using color markers, and the center of gravity (G), head (P ), hands (H1 and H2 ) and feet (F1 and F2 ) are detected by using the human silhouette. III. MOTION CAPTURE METHODS In this section, we show our proposed method of a motion capture. The followings show the overview of processes in this system (Fig.3): 1. Initialization. • Acquire a background image. • Estimate distances between human feature points for constraints. 2. Extract 2D coordinates of human feature points. Acquire human images by two cameras and • Extract marker positions using color information, • Extract other feature points using silhouette image, for each image. 3. Estimate 3D positions of feature points using stereo method. 4. Predict 3D coordinates of undetected feature points. 5. Modify 3D coordinates of feature points. 6. Iterate from 2 for continuous capturing. Details are shown in the followings. A. Color Marker Detection In general, a color image consists of the three primary colors, red, green and blue. But, this color representation has the problem of being easy to be influenced of change by brightness
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Abstract – In this paper, we discuss the motion capture system which acquire 3D human motion data using camera images. In this system, in order to detect feature points of human’s parts, we use a few color markers and silhouette images of human to be able to move human without restriction. And, we estimate 3D positions of feature points by the stereo method using two cameras. Moreover, when the feature points can not be detected by occlusions, proposed system predict and modify its locations using conditions of constraints about a human body. In the experimental results using real human images, we confirm that the 3D human motions can be acquired stably and accurately under occlusions. Keywords – motion capture, color marker, silhouette, stereo method
IMTC 2006 - Instrumentation and Measurement Technology Conference Sorrento, Italy 24-27 April 2006
Human Motion Capture System Using Color Markers and Silhouette
markers. But, when the same color objects exist in the surroundings of human, the positions of markers can not be detected correctly. Moreover, when a marker is hidden by a human body or some other objects, it will disappear in a camera image. Hence it is difficult to measure the position of the marker in such case. In this study, we discuss the method to measure 3D positions of the feature points in a human body using a few color markers and human silhouette[5]. And, in order to deal with the occlusion and incorrect detection of markers, we also discuss the method to predict and modify 3D positions of the feature points using the constraint conditions about the structure of the human body. In the experimental results, we execute the motion capture using a real video images, and we show the effectiveness of the proposed method which can obtain the 3D positions of the feature points accurately even if some of markers are not appeared in video images. II. MOTION CAPTURE SYSTEM AND HUMAN FEATURE POINTS Figure 1 shows the structure of motion capture system we use. The human images are taken by two video cameras which are located at fixed positions. These images are processed by