Recovering 3-D Human Body Postures from Depth Maps and Its Application in Human Activity Recognition

Recovering 3-D Human Body Postures from Depth Maps and Its Application in Human Activity Recognition

Nguyen Duc Thang (Kyung Hee University, Republic of Korea), Md. Zia Uddin (Kyung Hee University, Republic of Korea), Young-Koo Lee (Kyung Hee University, Republic of Korea), Sungyoung Lee (Kyung Hee University, Republic of Korea) and Tae-Seong Kim (Kyung Hee University, Republic of Korea)
Copyright: © 2012 |Pages: 22
DOI: 10.4018/978-1-61350-326-3.ch028


We present an approach of how to recover 3-D human body postures from depth maps captured by a stereo camera and an application of this approach to recognize human activities with the joint angles derived from the recovered body postures. With a pair of images captured with a stereo camera, first a depth map is computed to get the 3-D information (i.e., 3-D data) of a human subject. Separately the human body is modeled in 3-D with a set of connected ellipsoids and their joints: the joint is parameterized with the kinematic angles. Then the 3-D body model and 3-D data are co-registered with our devised algorithm that works in two steps: the first step assigns the labels of body parts to each point of the 3-D data; the second step computes the kinematic angles to fit the 3-D human model to the labeled 3-D data. The co-registration algorithm is iterated until it converges to a stable 3-D body model that matches the 3-D human posture reflected in the 3-D data. We present our demonstrative results of recovering body postures in full 3-D from continuous video frames of various activities with an error of about 60-140 in the estimated kinematic angles. Our technique requires neither markers attached to the human subject nor multiple cameras: it only requires a single stereo camera. As an application of our body posture recovery technique in 3-D, we present how various human activities can be recognized with the body joint angles derived from the recovered body postures. The features of body joints angles are utilized over the conventional binary body silhouettes and Hidden Markov Models are utilized to model and recognize various human activities. Our experimental results show the presented techniques outperform the conventional human activity recognition techniques.
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Through several million years of human evolution, stereopsis is one of the unique functions in the human vision system, allowing depth perception: it is a process of combining two images projected to two human eyes to create the visual perception of depth. Learned from the human stereoscopic system, a stereo camera was invented to synchronously capture two images of a scene with a slight difference in the view angle from which depth information of the scene can be derived. The depth information is generally reflected in a 2-D image called a depth map in which the depth information is encoded in a range of grayscale pixel values. Since its first commercial product in 1950s, Stereo Realist, introduced by the David White Company, there have been continuous developments of a stereo camera until now with the latest products such as a digital stereo camera, Fujifilm FinePix Real 3-D W1 and a stereo webcam, Minoru 3-D. Lately, 3-D movies, in which depth information is added to RGB images, have received a lot of attention with the latest success of a film, Avatar released in 2009. Watching 3-D movies and 3-D TVs with the special viewing glasses is becoming a part of our lives these days.

Another area where the depth information could be valuable is the field of human computer interaction (HCI). In this area, 3-D motion information of a user is utilized to better control external devices such as computers and games. In the conventional ways, capturing 3-D human motion or movement (i.e., a sequence of human postures) is typically done using optical markers or motion sensors. Such systems are capable of producing some kinematic parameters of human motion with high accuracy and speed using wearable optical markers or sensors. However, it is inconvenient to a user who needs to wear specially designed optical markers or sensor-suits when running these systems. This disadvantage combined with the high cost equipment makes the systems impractical in daily use applications. In the case of using motion sensors, a user has to hand-hold controllers equipped with accelerometers or gyroscopes. One good example is the Wii controller of Nintendo which uses optical sensors and accelerometers to recognize the hand motion of the user to control the games. Lately, some efforts are being made to capture the whole body movement without the markers or motion sensors. Using a stereo camera and its derived depth map is one of options, since depth maps may provide sufficient 3-D information to derive human body motions in 3-D. Although this approach should open a new possibility for various novel applications in HCI such as games and u-lifecare, obtaining human body postures in 3-D directly from depth maps is not very straightforward.

There have been some attempts to develop marker-less systems to estimate human motion from a sequence of monocular images or RGB images, only reflecting 2-D information. Because the 3-D information of the subject is lost, the efforts to reconstruct the 3-D motion of the subject from only monocular images face difficulties with ambiguity and occlusion that lead to inaccurate results (Yang & Lee, 2007). Therefore, most marker-less systems use multiple cameras to capture 3-D human motion. Through such systems, the 3-D information of the observed human subject is captured from different directional views, thereby providing better results of recovered human motion in 3-D (Knossow et al., 2008; Gupata et al., 2008). However, it is usually complicated to setup such a system, because it requires enough space where the cameras can be installed. Also it requires synchronization of the cameras. Thus, there are always some tradeoffs between the flexibility of using a single camera and the ability to get the 3-D information using multiple cameras.

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