We present a crossbreed feature-based head tracking technique in natural and unspecified environment. Kalman filter is a famous estimation technique in many areas to predict the route of moving object. We tested and developed a Kalman filter to track unpredictable and fast moving objects. Depth information could generate robust tracking result that is little affected by background texture and color. However this is also limited by selected conditions like distance, accuracy of stereo camera, and object occlusion at same distance, etc. To overcome these restrictions, we combined multiple features together into single tracking system that does largely depend on depth feature. We consider multi people environment with rapid walking path.
This paper presents our approach to using a stereo camera to obtain 3-D image data to be used to improve existing lip boundary detection techniques. We show that depth information as provided by our approach can be used to significantly improve boundary detection systems. Our system detects the face and mouth area in the image by using color, geometric location, and additional depth information for the face. Initially, color and depth information can be used to localize the face. Then we can determine the lip region from the intensity information and the detected eye locations. The system has successfully been used to extract approximate lip regions using RGB color information of the mouth area. Merely using color information is not robust because the quality of the results may vary depending on light conditions, background, and the human race. To overcome this problem, we used a stereo camera to obtain 3-D facial images. 3-D data constructed from the depth information along with color information can provide more accurate lip boundary detection results as compared to color only based techniques.