• The Journal of Korea Robotics Society
• /
• v.5 no.3
• /
• pp.262-269
• /
• 2010

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.

• Proceedings of the IEEK Conference
• /
• 2007.07a
• /
• pp.311-312
• /
• 2007

This dissertation proposes a global search and a local search method to track the object in real-time. The global search recognizes a target object among the candidate objects through the entire image search, and the local search recognizes and track only the target object through the block search. This dissertation uses the object color and feature information to achieve fast object recognition. Finally we conducted an experiment for the object tracking system based on a pan/tilt structure.

• The KIPS Transactions:PartB
• /
• v.16B no.5
• /
• pp.385-394
• /
• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Journal of Advanced Navigation Technology
• /
• v.14 no.2
• /
• pp.272-277
• /
• 2010

AR(Augmented Reality) needs medium between real and virtual, world, and recognition techniques are necessary to track an object continuously. Optical tracking using marker is mainly used, but it takes time and is inconvenient to attach marker onto the target objects. Therefore, many researchers try to develop markerless tracking techniques nowaday. In this paper, we extract features and 3D position from 3D objects and suggest realtime tracking based on these features and positions, which do not use just coplanar features and 2D position. We extract features using SURF, get rotation matrix and translation vector of 3D object using POSIT with these features and track the object in real time. If the extracted features are nor enough and it fail to track the object, then new features are extracted and re-matched to recover the tracking. Also, we get rotation in matrix and translation vector of 3D object using POSIT and track the object in real time.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.201-206
• /
• 1996

This paper presents a new information processing machine which is called artificial brain(ABrain) and considers the structure of artificial neural networks constructed in a RICOH neurocomputer RN-2000 in the ABrain, in order to track given trajectories which are produced in a micro-computer or a moving light by hand in a recognition and tracking system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2010.07a
• /
• pp.42-45
• /
• 2010

This paper proposes an intelligent video surveillance system for human object tracking. The proposed system integrates the object extraction, human object recognition, face detection, and camera control. First, the object in the video signals is extracted using the background subtraction. Then, the object region is examined whether it is human or not. For this recognition, the region-based shape descriptor, angular radial transform (ART) in MPEG-7, is used to learn and train the shapes of human bodies. When it is decided that the object is human or something to be investigated, the face region is detected. Finally, the face or object region is tracked in the video, and the pan/tilt/zoom (PTZ) controllable camera tracks the moving object with the motion information of the object. This paper performs the simulation with the real CCTV cameras and their communication protocol. According to the experiments, the proposed system is able to track the moving object(human) automatically not only in the image domain but also in the real 3-D space. The proposed system reduces the human supervisors and improves the surveillance efficiency with the computer vision techniques.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.135-135
• /
• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks that have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, we have a global search for entire image and then have tracking the object through local search when the object is recognized.

• Journal of the Korea Society of Computer and Information
• /
• v.7 no.3
• /
• pp.49-55
• /
• 2002

This paper proposes speaker recognition algorithm which includes both the pitch parameter and the fuzzy. This study proposes a pitch detection method for the peak and valley pitch detection function by means of comparing spectra which utilizes the transform characteristics between time and frequency. It measures the similarity to the original spectrum while arbitrarily varying the period in the time domain. It heavily weights the error due to the changing characteristics of the phonemes, while it is strong against noise. In this paper, makes reference pattern using membership function and performs vocal track recognition of common character using fuzzy pattern matching in odor to include time variation width for non-linear utterance time.

• Journal of the Korea Society of Computer and Information
• /
• v.5 no.3
• /
• pp.51-57
• /
• 2000

This paper proposes speaker recognition algorithm which includes both the pitch and the fuzzy. This study proposes a pitch detection method for the peak and valley pitch detection function by means of comparing spectra which utilizes the transform characteristics between time and frequency. It measures the similarity to the original spectrum while arbitrarily varying the period in the time domain. It heavily weights the error due to the changing characteristics of the phonemes, while it is strong against noise. In this paper, makes reference pattern using membership function and performs vocal track recognition of common character using fuzzy pattern matching in order to include time variation width for non-linear utterance time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.513-515
• /
• 2019

Techniques to recognize depth values using Kinect infrared projectors continue to evolve. Techniques to track human movements are being developed from the Marcris method to the Bimarris method. Capture of facial movement using Kinect has disadvantages that are not sophisticated. In addition, a method to control the gestures and movements on the face in real time requires much research. Therefore, this paper proposes a technique to create natural 3D image contents by studying technology to apply and control branding technology to extracted face recognition data using Kinect infrared method.