• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.43-53
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• 2012

In the this paper, a gesture-based user interface based on object detection using predefined gesture and the tracking of the detected object is proposed. For object detection, moving objects in a frame are computed by comparing multiple previous frames and predefined gesture is used to detect the target object among those moving objects. Any object with the predefined gesture can be used to control. We also propose an object tracking algorithm, namely density based meanshift algorithm, that uses color distribution of the target objects. The proposed object tracking algorithm tracks a target object crossing the background with a similar color more accurately than existing techniques. Experimental results show that the proposed object detection and tracking algorithms achieve higher detection capability with less computational complexity.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.9-14
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• 1999

The object boundary of an image plays an important role for image interpretation. In this paper, we introduce a concept of hierarchical mesh-based image segmentation for finding object boundaries. In each hierarchical layer, we employ neighborhood searching and boundary tracking methods to refine the initial boundary estimate. We also apply a local region growing method to define closed contours. Experimental results indicate that reliable segmentation of objects can be accomplished by the pro-posed tow complexity technique.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.307-312
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• 2001

In this paper, we proposed the object-tracking algorithm that recognizes and estimates the any shaped and size objects using vision system. For the extraction of the object from the background of the acquired images, spatio-temporal filter and signature parsing algorithm are used. Specially, for the solution of correspondence problem of the multiple objects tracking, we compute snake energy and position information of the target objects. Through the real-time tracking experiment, we verified the effectiveness of the suggested tracking algorithm.

• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.782-791
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• 2016

The object segmentation algorithm from the background could be used for object recognition and tracking, and many applications. To segment objects, this paper proposes a method that refer to several initial frames with real-time processing at fixed camera. First we suggest the probability model to segment object and background and we enhance the performance of algorithm analyzing the color consistency and focus characteristic of camera for several initial frames. We compensate the segmentation result by using human skeleton characteristic among extracted objects. Last the proposed method has the applicability for various mobile application as we minimize computing complexity for real-time video processing.

• Annual Conference of KIPS
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• 2002.11a
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• pp.669-672
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• 2002

최근 멀티미디어 기술의 급속한 발달로 인해 개인의 신원 확인, 보안 시스템 등의 영역에서 얼굴과 관련된 연구가 활발히 진행 되고 있다. 기존의 연구에서는 원거리 추적이 어려우며, 연산시간, 잡음(noise), 배경과 조명등에 따라 추적 효율이 낮은 단점을 가지고 있다. 본 논문에서는 빠르고 정확한 얼굴 추적을 위한 차 영상 기법(differential image method)을 이용한 분할영역(segmentation region)에서 움직임(motion)과 피부색(skin color) 특성 기반의 객체분할추적(Tracking Of Object segmentation) 방법을 이용하였다. 객체분할추적은 얼굴을 하나의 객체(object)로 인식하고 제안한 방법으로 얼굴 부분만 분할하는 단계와 얼굴특징추출 단계를 적용하여 피부색 기반의 연구에서 나타난 입력영상(Current Frame)에서의 유동적인 피부색의 노출 대한 얼굴 추적 연구의 문제점을 해결했다. 시스템은 현재 컴퓨터에 일반적으로 사용되는 카메라를 이용하여 구현 하였고, 실시간(real-time) 영상에서 비교적 성공적인 얼굴 추적을 하였다[4].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.574-578
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• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.219-222
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• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.113-119
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• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.61-67
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• 2017

Recently, CCTV has been combined with areas such as big data, artificial intelligence, and image analysis to detect various abnormal behaviors and to detect and analyze the overall situation of objects such as people. Image analysis research for this intelligent video surveillance function is progressing actively. However, CCTV images using 2D information generally have limitations such as object misrecognition due to lack of topological information. This problem can be solved by adding the depth information of the object created by using two cameras to the image. In this paper, we perform background modeling using Mixture of Gaussian technique and detect whether there are moving objects by segmenting the foreground from the modeled background. In order to perform the depth information-based segmentation using the RGB information-based segmentation results, stereo-based depth maps are generated using two cameras. Next, the RGB-based segmented region is set as a domain for extracting depth information, and depth-based segmentation is performed within the domain. In order to detect the center point of a robustly segmented object and to track the direction, the movement of the object is tracked by applying the CAMShift technique, which is the most basic object tracking method. From the experiments, we prove the efficiency of the proposed object detection and tracking method using the RGB-D model.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.54-64
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• 2002

In this paper, we propose the algorithm for segmenting the moving speaker and tracking its movement in the video conference image. For real time processing, we simplify the algorithm which is processed in the order of the segmenting and the tracking step. In the segmenting step, the speaker object is segmented from the image by using both the motion information obtained from the difference method and the illuminance information of image. The reference mask image is created from segmented speaker object. In the tracking step, the moving speaker is tracked by using simple block matching algorithm of which computation time is reduced by discarding the blocks which are classified into the unuseful blocks. In the simulation, we can get the good result of segmenting and tracking the moving speaker by applying the proposed algorithm to several test images.