Real-time Video Information Retrieval and Environment Invariant User Gesture Recognition Algorithm Development

2008 . 08 ~ Current

 

1. Introduction
In the Human Robot Interaction (HRI), it is required for intelligent robots to recognize the outer state, and make decisions in order to successfully interact with humans. Therefore it is necessary to carry out user detection in the real time process for an effective HRI. To carry out user detection in the real time process, partial decoding and compression domain approach is performed. The extracted visual information will be used to detect face, torso and hand as well as tracking of those user body parts will allow successful user detection and recognition.

The purpose of this project is to detect face, torso and hand of user as well as to recognize the user movements and gestures by using the compressed video produced by the embedded robot. After robot acquires the visual information, it uses compressed format such as MPEG to send the information to the server through network. To save computation load as well as the cost of bandwidth, the compressed domain approach is necessary. The specific goals of this project are as follows.

1)Extracting visual information required for user detection in real-time from the MPEG Compressed domain

2)Developing environment invariant face detection and tracking algorithm, that is illumination and other characteristics of robot are considered

3)Developing gesture recognition with the tracked object information

2. Robot vision system
 Since the embedded robot uses server to perform user detection processing, this is a client/server based architecture known as network robot. The robot perceives visual information through two cameras. Then it compresses the information using MPEG-4 encoder then sends it to the server through USB/Ethernet. When images are received, stereo image is created to provide additional information.

The following diagram shows the overall robot vision system and the processing part in the server.

 

   Fig. 1.      The robot vision system

As shown in figure 1, the robot vision system consists of the embedded robot, USB/Ethernet (Network) and the server. The embedded robot mainly perceives images through two cameras, and forms two stereo images that can be used later. Also, These images are compressed using MPEG-4 compression, and transmitted to the server using USB/Ethernet. In the server side, the bitstream analyzer is used to extract necessary information and proceeds to detection and tracking module.

 

 

 

              

 Fig. 2. MPEG-4 bitstream Analyzer

 

The bitstream analyzer has three functions to carry out as seen in Fig 2. It receives MPEG-4 bitstream produced by the robot in UDP communication protocols. Then it uses FFMPEG library to partially decode received frame. Then extracted DCT parameters and Motion vectors are later used for user detection and tracking.

Fig 3. illustrates the detailed decoding module. The project uses FFMPEG library as mentioned for decoding bitstream. The MPEG-4 header information to be parsed is generated. The system header including image resolution, codec information, GoP size as well as visual data from the embedded system. Also, the system uses memory management to store MPEG-4 frame for real-time processing. The same memory location is allocated for every delivered frame. After storing one frame data, they are also sent to ffmpeg directly in real-time. A length of MPEG-4 data is unfixed as it is with the memory too.

 

 

 

Fig. 3. Decoding module using FFMPEG library

3.  User Detection

Once, the necessary information is extracted, the user detection is performed by first taking face detection process. Since user detection requires face information to start with, face is first detected from the given information. The DC coefficients extracted from the above procedure will be used to form a DC image, which will be used as an input for the face detection system that works directly under the compressed domain as well as invariant to other environmental factors including illuminations and camera characteristics. The overview of the face detection system is as follows.

 

                                                            

                Fig. 4. Overview of the face detection system

 

When DC image is formed, it requires consistent characteristics and illumination compensated information for reliable face detection results, hence, color balancing and illumination compensation are performed. Color Balancing is performed using the color temperature conversion. Color temperature conversion is performed by using the MPEG-7 descriptor. Basically, temperature of the DC image is estimated using CIE 1960 UCS diagram, and converted to target temperature using Bradford chromatic adaptation. The target temperature is set to 5500K by taking into consideration the daylight and under electronic lights which have temperature of 5500K. This would allow consistent characteristics for all robot cameras, since different information is obtained with the same objects. Then, illumination compensation is performed to provide reliable face detection. Often illumination factors are significant in color based face detection approach, since it corrupts skin color information. Therefore, illuminations must be compensated, and in this project, Retinex algorithm was employed to eliminate illumination components. Retinex algorithm is based on the human visual system proposed by Land, which effectively eliminates illuminations by leaving reflectance components only in the image. Then reliable skin colors can be detected for the next steps. Once skin color region is matched by using proven skin color model, the face template is used to match against any skin color region. If there is a match, the region is selected as face candidate region. The face candidate regions must be verified using two properties of face: 1) All face has details and edge information  2) All face has more intensity change in the direction of vertical rather than horizontal. These two properties can be used by calculating the energy distributed in that face candidate region. To do so, AC coefficients extracted earlier with the DC coefficients, are used for this energy calculation. Finally, if the criteria are met, then the candidate region is finally known as face region.

 

Fig. 5. Torso mask and Torso detection results

With the acquired face region information, torso region can be decided. In addition, stereo image can be used to verify the position of the torso. The torso mask used in this project is based on Virtuvian torso, which states that the torso has the twice size of the face. Therefore, when face information is used torso can be predicted. Also, stereo image distinguishes foreground and background where foreground can be used to verify that the torso is taking place. The fig. 5 shows the torso mask and the results of torso detection and face detection.

 

Then hand region can be easily detected. Because hand also contains skin color information, any skin color region rejected by the face candidate, can be used to predict the hand. However, there may be an error or noise, therefore in order to be sure about the hand, it requires  certain amount of skin color regions beside the predicted region due to the fact, hand also takes portion in the image. The following figure is the results of hand detection. and it concludes the user detection module.

 

 

                                                                  

Fig.6. The detected face, torso and hand of the user

 

3.  User Tracking

The user tracking can be performed when user detection has been accomplished. The detection takes in I frame, therefore in P frames, tracking will be performed with the motion vector extracted before, and each detected regions.  The following diagram clearifys the Intra VOP and Inter VOP detection and tracking.

 

Fig.7. Tracking module for face region

 

 

The above example shows the tracking of the face region. Likewise, the region of interest (face, torso or hand) is passed onto the tracking module. 

To be more specific, the object tracking is performed as follows.

 

 

Fig. 8. Specific description of tracking

 

User detection and tracking is peformed as mentioned above. And user recognition using detected information will be carried out by analyzing the coordinates of the hand, elbow and other body parts based on the face, torso information. This is currently under research.

 

* Contact Person: Prof. Yong Man Ro (ymro@kaist.ac.kr)

 

“Design of Object Detection System in Video Compressed Domain for An Intelligent Robot System,” Hyun-Seok Min, Young Bok Lee, Yong Man Ro, TriSAI 2008, Oct.6~9, 2008, DaeJeon, Korea

압축 영역에서의 효율적인 얼굴 검출을 위한 조명 효과 개선에 관한 연구”, 이영복, 민현석 노용만, 2008 대한전자공학회 추계학술대회, pp.677-688, 연세대학교, November 29, 2008

"압축 영역에서 동작하는 조명 환경 변화에 강인한 얼굴 검출 방법에 관한 연구", 민현석이영복노용만, 2008 HCI 학술대회 (submitted)