• Journal of information and communication convergence engineering
• /
• v.13 no.3
• /
• pp.197-204
• /
• 2015

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective.

• Journal of Korea Multimedia Society
• /
• v.11 no.5
• /
• pp.597-609
• /
• 2008

Due to the recursive updating nature of background model, previous background modeling methods are often perturbed by crowd scenes where foreground pixels occurs more frequently than background pixels. To resolve this problem, an adaptive background modeling method, which is based on the well-known Gaussian mixture background model, is proposed. In the proposed method, the learning rate of background model is adaptively adjusted with respect to the crowdedness of the scene. Consequently, the learning process is suppressed in crowded scene to maintain proper background model. Experiments on real dataset revealed that the proposed method could perform background subtraction effectively even in crowd situation while the performance is almost the same to the previous method in normal scenes. Also, the F-measure was increased by 5-10% compared to the previous background modeling methods in the video of crowded situations.

• Journal of Korea Multimedia Society
• /
• v.12 no.10
• /
• pp.1418-1426
• /
• 2009

There has been a lot of interest in an effective method for background subtraction in an effort to separate foreground objects from a predefined background image. Promising results on background subtraction using statistical methods have recently been reported are robust enough to operate in dynamic environments, but generally require very large computational resources and still have difficulty in obtaining clear segmentation of objects. We use a simple running-average method to model a gradually changing background, instead of using a complicated statistical technique. We employ a single global threshold vector, optimized by a genetic algorithm, instead of pixel-by-pixel thresholds. A new fitness function is defined and trained to evaluate segmentation result. The system has been implemented on a PC with a webcam, and experimental results on real images show that the new method outperforms an existing method based on a mixture of Gaussian.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.04a
• /
• pp.357-360
• /
• 2007

물체를 추적하는데 있어서 추적하고자 하는 물체를 검출하여 템플릿을 만드는 것과 두 물체가 겹쳐지거나 다른 배경에 가려진 물체를 구분하여 추적하는 것은 물체 추적에 있어서 중요한 문제이다. 물체를 검출하여 템플릿을 만드는 방법으로 frame difference를 이용하면 천천히 움직이는 물체를 잘 구분할 수 없는 문제점이 있다. 이를 해결하기 위하여 본 논문에서는 adaptive 3-frame difference를 이용하여 정확한 물체의 템플릿을 생성하는 알고리즘을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.3
• /
• pp.349-354
• /
• 2007

To generate the template of a detected object and to track the overlapped object and the object covered by other objects correctly are important research problems in visual surveillance. The frame difference is not capable of generating the template of slowly moving object. To get around the drawback of the conventional frame difference, we propose a new algorithm for generating a template using adaptive 3-frame difference.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.3
• /
• pp.11-19
• /
• 2008

Background subtraction, which separates moving objects in video sequences, is an essential technology for object recognition and tracking. However, background subtraction methods are often confused by shadow regions and this misclassification of shadow regions disturbs further processes to perceive the shapes or exact positions of moving objects. This paper proposes a method for shadow elimination which is based on shadow modeling by color information and Bayesian classification framework. Also, because of dynamic update of modeling parametres, the proposed method is able to correspond adaptively to illumination changes. Experimental results proved that the proposed method can eliminate shadow regions effectively even for circumstances with varying lighting condition.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.4
• /
• pp.73-81
• /
• 2011

In this paper, an improved block-based background modeling technique using adaptive parameter estimation that judiciously adjusts the number of model histograms at each frame sequence is proposed. The conventional block-based background modeling method has a fixed number of background model histograms, resulting to false negatives when the image sequence has either rapid illumination changes or swiftly moving objects, and to false positives with motionless objects. In addition, the number of optimal model histogram that changes each type of input image must have found manually. We demonstrate the proposed method is promising through representative performance evaluations including the background modeling in an elevator environment that may have situations with rapid illumination changes, moving objects, and motionless objects.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.55-62
• /
• 2014

In the intelligent video surveillance system, moving objects generally are detected by calculating difference between background and input image. However formation of reliable background is known to be still challenging task because it is hard to cope with the complicated background. In this paper we propose an adaptive background formation algorithm considering stationary object. At first, the initial background is formed by averaging the initial N frames. Object detection is performed by comparing the current input image and background. If the object is at a stop for a long time, we consider the object as stationary object and background is replaced with the stationary object. On the other hand, if the object is a moving object, the pixels in the object are not reflected for background modification. Because the proposed algorithm considers gradual illuminance change, slow moving object and stationary object, we can form background adaptively and robustly which has been shown by experimental results.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2013.07a
• /
• pp.49-50
• /
• 2013

본 논문에서는 물체탐지를 위한 적응적 배경 생성 기법을 제안한다. 연속적으로 입력되는 영상들의 통계적 평균을 이용하여 배경을 생성하고 배경과 입력영상간의 차영상을 구하여 물체를 탐지한다. 탐지된 물체를 추척하여 일정시간이상 계속 정지해 있는 경우에는 그 물체영역을 배경으로 갱신하고, 이동 물체인 경우에는 배경 갱신에서 배제함으로써 지속적으로 물체를 탐지할 수 있도록 한다. 실험결과는 제안된 방법의 강건함을 보인다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06b
• /
• pp.307-309
• /
• 2006

움직이는 오브젝트(Object)를 추출하기 위한 배경 제거(Background Subtraction) 단계는 실시간 감시시스템(Real-time Surveillance System)에서 중요한 과정 중에 하나이다. 배경 제거를 효과적으로 진행하기 위한 배경을 모델링, 배경 유지 보수 방법이 존재하는데, 효율성이 높은 방법으로 적응적 가우시안 혼합 배경 모델링(Adaptive Gaussian Mixture Background Modeling)이 제시되고 있다. 본 논문에서는 이기법을 바탕으로 하여 이러한 실시간 배경 모델링 시스템을 구현하려 하고, 중앙 처리 장치(CPU)가 아닌 그래픽 처리 장치(Graphic Processing Units : GPU)를 사용하여 보다 향상된 방범을 구현함으로서 관련사항을 제안하려 한다.