Humans and robots may acquire knowledge by observing demonstrations of object manipulation in scenarios ranging from everyday tasks, such as tieing laces and executing a recipe, to critical operations like surgery, electronics (dis-)assembly, etc. As the corpus of related videos is enlarged and the knowledge extraction becomes mission critical, the automation of the knowledge extraction process...

The recognition of hand gestures from image sequences is an important and challenging problem. This paper presents a robust solution to track and recognize a list of hand gestures from their trajectory. The CamShift algorithm is used for hand tracking and the resulting trajectory is segmented into strokes. The trajectory of recognized gestures consists of at least 2 strokes. The gestures are cl...

In this paper, we develop an effective method of detecting and tracking hands in uncontrolled videos based on multiple cues including hand shape, skin color, upper body position and flow information. We apply our hand detection results to perform fine-grained human action recognition. We demonstrate that motion features extracted from hand areas can help classify actions even when they look fam...

A computer vision system is described that captures color image sequences, detects and recognizes static hand poses (i.e., “letters”) and interprets pose sequences in terms of gestures (i.e., “words”). The hand object is detected with a double-active contour-based method. A tracking of the hand pose in a short sequence allows detecting “modified poses”, like diacritic letters in national alphab...

In this paper, we propose real-time hand tracking with a depth camera by using a Kalman Filter and an improved DAM-Shift(Depthbased adaptive mean shift) algorithm for occlusion handling. DAM-Shift is a useful algorithm for hand tracking, but difficult to track when occlusion occurs. To detect the hand region, we use a classifier that combines a boosting and a cascade structure. To verify occlus...

The created vision system captures image sequences from the digital camera and it first detects static hand poses in every single frame due to a doubleactive contour classification. The tracking of the hand pose in a short sequence allows to detect ”modified poses”, like diacritic letters of polish alphabet. Finally, by tracking hand poses in a longer image sequence, this pose sequence ic class...

Computer sensing of hand and limb motion is an important problem for applications in HumanComputer Interaction (HCI), virtual reality, and athletic performance measurement. Commercially available sensors are invasive, and require the user to wear gloves or targets. We have developed a noninvasive vision-based hand tracking system, called DigitEyes. Employing a kinematic hand model, the DigitEye...

Sign language (SL) recognition, although has been explored for many years, is still a challenging problem for real practice. The complex background and illumination conditions affect the hand tracking and make the SL recognition very difficult. Fortunately, Kinect is able to provide depth and color data simultaneously, based on which the hand and body action can be tracked more accurate and eas...

MIME (Mime Is Manual Expression) is a computationally efficient computer vision system for recognizing hand gestures. The system is intended to replace the mouse interface on a standard personal computer to control application software in a more intuitive manner. The system is implemented in C code with no hardware-acceleration and tracks hand motion at 30 fps on a standard PC. Using a simple t...