• Title/Summary/Keyword: Moving Shadow Detection

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Real-Time Moving Object Detection and Shadow Removal in Video Surveillance System (비디오 감시 시스템에서 실시간 움직이는 물체 검출 및 그림자 제거)

  • Lee, Young-Sook;Chung, Wan-Young
    • 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.

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Moving Shadow Detection using Deep Learning and Markov Random Field (딥 러닝과 마르코프 랜덤필드를 이용한 동영상 내 그림자 검출)

  • Lee, Jong Taek;Kang, Hyunwoo;Lim, Kil-Taek
    • Journal of Korea Multimedia Society
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    • v.18 no.12
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    • pp.1432-1438
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    • 2015
  • We present a methodology to detect moving shadows in video sequences, which is considered as a challenging and critical problem in the most visual surveillance systems since 1980s. While most previous moving shadow detection methods used hand-crafted features such as chromaticity, physical properties, geometry, or combination thereof, our method can automatically learn features to classify whether image segments are shadow or foreground by using a deep learning architecture. Furthermore, applying Markov Random Field enables our system to refine our shadow detection results to improve its performance. Our algorithm is applied to five different challenging datasets of moving shadow detection, and its performance is comparable to that of state-of-the-art approaches.

An Effective Moving Cast Shadow Removal in Gray Level Video for Intelligent Visual Surveillance (지능 영상 감시를 위한 흑백 영상 데이터에서의 효과적인 이동 투영 음영 제거)

  • Nguyen, Thanh Binh;Chung, Sun-Tae;Cho, Seongwon
    • Journal of Korea Multimedia Society
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    • v.17 no.4
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    • pp.420-432
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    • 2014
  • In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

Color Intensity Variation based Approach for Background Subtraction and Shadow Detection

  • Erdenebatkhaan, Turbat;Kim, Hyoung-Nyoun;Lee, Joong-Ho;Kim, Sung-Joon;Park, Ji-Hyung
    • 한국HCI학회:학술대회논문집
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    • 2007.02a
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    • pp.298-301
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    • 2007
  • Computational speed plays key role in background subtraction and shadow detection, because those are only preprocessing steps of a moving object segmentation, tracking and activity recognition. A color intensity variation based approach fastly detect a moving object and extract shadow in a image sequences. The moving object is subtracted from background using meanmax, meanmin thresholds and shadow is detected by decrease limit and correspondence thresholds. The proposed approach relies on the ability to represent shadow cast impact by offline experiment dataset on sub grouped RGB color space.

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Shadow Removal Based on Chromaticity and Entropy for Efficient Moving Object Tracking (효과적인 이동물체 추적을 위한 색도 영상과 엔트로피 기반의 그림자 제거)

  • Park, Ki-Hong
    • Journal of Advanced Navigation Technology
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    • v.18 no.4
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    • pp.387-392
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    • 2014
  • Recently, various research for intelligent video surveillance system have been proposed, but the existing monitoring systems are inefficient because all of situational awareness is judged by the human. In this paper, shadow removal based moving object tracking method is proposed using the chromaticity and entropy image. The background subtraction model, effective in the context awareness environment, has been applied for moving object detection. After detecting the region of moving object, the shadow candidate region has been estimated and removed by RGB based chromaticity and minimum cross entropy images. For the validity of the proposed method, the highway video is used to experiment. Some experiments are conducted so as to verify the proposed method, and as a result, shadow removal and moving object tracking are well performed.

Removing Shadows Using Background Features in the Images of a Surveillance Camera (감시용 카메라 영상에서의 배경 특성을 사용한 그림자 제거)

  • Kim, Jeongdae;Do, Yongtae
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.3
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    • pp.202-208
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    • 2013
  • In the image processing for VS (Video Surveillance), the detection of moving entities in a monitored scene is an important step. A background subtraction technique has been widely employed to find the moving entities. However, the extracted foreground regions often include not only real entities but also their cast shadows, and this can cause errors in following image processing steps, such as tracking, recognition, and analysis. In this paper, a novel technique is proposed to determine the shadow pixels of moving objects in the foreground image of a VS camera. Compared to existing techniques where the same decision criteria are applied to all moving pixels, the proposed technique determines shadow pixels using local features based on two facts: First, the amount of pixel intensity drop due to a shadow depends on the intensity level of background. Second, the distribution pattern of pixel intensities remains even if a shadow is cast. The proposed method has been tested at various situations with different backgrounds and moving humans in different colors.

Vehicle Shadow Detection in Thermal Videos (열 영상에서의 차량 그림자 제거 기법)

  • Kim, Ji-Man;Choi, Eun-Ji;Lim, Jeong-Eun;Noh, Seung-In;Kim, Dai-Jin
    • Proceedings of the Korean Information Science Society Conference
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    • 2012.06b
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    • pp.369-371
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    • 2012
  • Shadow detection and elimination is a critical issue in vision-based system to improve the detection performance of moving objects. However, traditional algorithms are useless at night time because they used the chromaticity and brightness information from the color image sequence. To obtain the high detection performance, we can use the thermal camera and there are shadows by the heat not the light. We proposed a novel algorithm to detect and eliminate the shadows using the thermal intensity and the locality property. By combining two results of the intensity-based and locality-based, we can detect the shadows by the heat and improve the detection performance of moving object.

Fuzzy Based Shadow Removal and Integrated Boundary Detection for Video Surveillance

  • Niranjil, Kumar A.;Sureshkumar, C.
    • Journal of Electrical Engineering and Technology
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    • v.9 no.6
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    • pp.2126-2133
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    • 2014
  • We present a scalable object tracking framework, which is capable of removing shadows and tracking the people. The framework consists of background subtraction, fuzzy based shadow removal and boundary tracking algorithm. This work proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects, and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects and shadows are processed differently in order to supply an object-based selective update. Experimental results demonstrate that the proposed method is able to track the object boundaries under significant shadows with noise and background clutter.

Efficient Learning and Classification for Vehicle Type using Moving Cast Shadow Elimination in Vehicle Surveillance Video (차량 감시영상에서 그림자 제거를 통한 효율적인 차종의 학습 및 분류)

  • Shin, Wook-Sun;Lee, Chang-Hoon
    • The KIPS Transactions:PartB
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    • v.15B no.1
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    • pp.1-8
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    • 2008
  • Generally, moving objects in surveillance video are extracted by background subtraction or frame difference method. However, moving cast shadows on object distort extracted figures which cause serious detection problems. Especially, analyzing vehicle information in video frames from a fixed surveillance camera on road, we obtain inaccurate results by shadow which vehicle causes. So, Shadow Elimination is essential to extract right objects from frames in surveillance video. And we use shadow removal algorithm for vehicle classification. In our paper, as we suppress moving cast shadow in object, we efficiently discriminate vehicle types. After we fit new object of shadow-removed object as three dimension object, we use extracted attributes for supervised learning to classify vehicle types. In experiment, we use 3 learning methods {IBL, C4.5, NN(Neural Network)} so that we evaluate the result of vehicle classification by shadow elimination.

Head Position Detection Using Omnidirectional Camera (전 방향 카메라 영상에서 사람의 얼굴 위치검출 방법)

  • Bae, Kwang-Hyuk;Park, Kang-Ryoung;Kim, Jai-Hie
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.283-284
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    • 2007
  • This paper proposes a method of real-time segmentation of moving region and detection of head position in a single omnidrectional camera Segmentation of moving region used background modeling method by a mixture of Gaussian(MOG) and shadow detection method. Circular constraint was proposed for detecting head position.

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