Multiple Background Model Research Articles

다중 배경모델과 순시적 중앙값 배경모델을 이용한 불안정 상태 카메라로부터의 실시간 이동물체 검출

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

색상 분포 및 인체의 상황정보를 활용한 다중카메라 기반의 사람 대응

In this paper, we proposed a method which corresponds people under the structured spaces with multiple cameras. The correspondence takes an important role for using multiple camera system. For solving this correspondence, the proposed method consists of three main steps. Firstly, moving objects are detected by background subtraction using a multiple background model. The temporal difference is simultaneously used to reduce a noise in the temporal change. When more than two people are detected, those detected regions are divided into each label to represent an individual person. Secondly, the detected region is segmented as features for correspondence by a criterion with the color distribution and context information of human body. The segmented region is represented as a set of blobs. Each blob is described as Gaussian probability distribution, i.e., a person model is generated from the blobs as a Gaussian Mixture Model (GMM). Finally, a GMM of each person from a camera is matched with the model of other people from different cameras by maximum likelihood. From those results, we identify a same person in different view. The experiment was performed according to three scenarios and verified the performance in qualitative and quantitative results.

REAL-TIME OBJECT DETECTION USING TWO BACKGROUND MODELS UNDER SHAKING CAMERA

In this paper, we propose a novel approach to detect moving objects by two background models, multiple background model (MBM) and temporal median background (TMB), from hand-taken image sequence. For this purpose, we record image sequences by hand-held camera without tripod so every frame has variation between consecutive frames. A pixel-based background model is fragile while image sequence has variation. Therefore we calculate the camera movement using correlation between two consecutive images and it helps us to generate MBM under shaking camera. The computational cost of correlation quickly increases if image resolution increases. Hence, we use edge segments to reduce computational cost. These edge segments are gathered by Sobel operator and those are distinctive spatial features to calculate similarity between two regions, belonging to current and previous images, organized by neighbors of edge segments. Based on the similarity result, we obtain a set of best matched regions, centroids of matched regions, and displacement vectors from each pair of previous and current images. Each displacement vector in a set describes the transition of each matched region in the image pair. Using the highest density of displacement vector histogram, we choose the camera motion vector, indicates camera movement between consecutive frames. According to the camera motion vector, every pixel in a current image is related to different position pixels in a previous image. The pixel relation is used to generate MBM in this paper, unlike original MBM [Xiao, M., Han, C. and Kang, K. [2006]. Proc. Int. Conf. Information Fuscon, pp. 1–7.]. The MBM algorithm classifies the variation of pixel values in frame sequence to several clusters. Classification of varying pixel values to several clusters is similar with mixture of gaussian (MOG). Nevertheless, MBM has low cost to calculate because it does not need to estimate parameter. However, MBM is not sensitive to short period changes. Therefore, we use TMB to support MBM. The experimental result shows that proposed algorithm successfully detects moving objects using background subtraction less than 25 ms per frame when camera has 2D translation.

Human Tracking based on Multiple View Homography

We propose a method for detection and tracking for objects under multiple cameras system. To track objects, one need to establish correspondence objects among multiple views. We apply the principal axis of objects and the homography constraint to match objects across multiple cameras. The principal axis belongs to the silhouette of objects that is extracted by the background subtraction. We use the multiple back- ground model to the background subtraction. In an image sequence, many changes happen with respect to pixel intensity. This cannot be characterized by the single background model so that is necessary to use the multiple background model. Also, we use the median background model reducing some noises. The silhouette is detected by difference with background models and current image which includes moving objects. For calculating homography, we use landmarks on the ground plane in 3D space. The homography means the relation between two correspondence between two coinciding points from different views. The intersection of principal axes and ground plane in 3D space are the same point shown in each view. The intersection occurs when a principal axis in an image crosses to the transformed ground plane from another image. We con- struct the correspondence which means the relationship between intersection in current image and transformed intersection from the other image by homography constraint. Those correspondences confirm within a short distance measuring in the top viewed plane. Thus, we track a person by these corresponding points on the ground plane.

Multiple Camera-based Correspondences of People using Camera Networks and Context Information

Abstract This paper proposes a method to correspondence a same person from different views. To design a system with multi vision sensors, we should to solve the correspondence problem. For obtain a correspondence, a proposed method has three steps. The first step is to detect moving objects by background subtraction from multiple background model. The background model is generated from image sequence and it updates by itself in each process. The temporal difference is used jointly to compensate a limitation of multiple background model. The detected silhouettes are divided as individual by labeling when more than two people are detected in an image. The second step is to generate blobs from detected silhouette. The silhouette is segmented as blobs by a criterion with color range and context information of human body. A person is composed of a set of blobs. The blob set is represented as a model by estimation of Gaussian mixture model (GMM). In the final step, a GMM of detected person from a camera is matched with other GMMs from different cameras to identify a same person.

DETECTION AND ANALYSIS OF MOVING OBJECTS FOR VIDEO SURVEILLANCE

In this paper, we address the problem of detection and analysis of moving objects in a video stream obtained by a fixed camera. To detect the moving objects, the tradition method is to create a fixed image first, which includes all the motionless parts of the scene, known as the background model. The difficulty of this approach lies mainly in two aspects: The first relates to the fact that a slow moving object can leave a visible trace in background model. The latter comes from the variation of illumination in the course of time so it cannot obtain a reasonable background model. To overcome these difficulties, we propose a multiple background model. At the exit of the detection of moving objects, the tracking (matching) of a moving object extracted in the successive images is necessary to analyze its behavior. After the matching of mobile objects, a series of analysis methods are presented. The proposed tracking and analysis methods allow dealing with partial occlusions, stopping and going motion, moving directions, crossing of moving object in very challenging situations. The experiment and comparison results are reported for different real sequences, which show better performance of our methods.