1,721,054 research outputs found
A Block Based Moving Object Detection Utilizing the Distribution of Noise
No full textMoving object segmentation in complex scene is the basis for video surveillance, event detection, tracking and development of vision agent in industrial robotics. However, due to presence of camera noise and illumination change, simple background subtraction based techniques are not able to detect moving objects properly. In this paper, we present a novel block based moving object detection method which dynamically quests for both local and global properties of difference image to achieve robustness. Noise model of the difference image is determined analyzing the histogram of difference image and block wise local properties are computed. These local properties are compared with the noise model to extract moving blocks. To remove the stair like artifacts of the segmented result, and to obtain smoothed and accurate boundary, a refinement procedure is employed on the boundary regions of detected moving objects. Experimental results show that the proposed method is robust and achieves better performance in dynamic environment.</p
Segmentation of moving object for content based applications
No full textThis paper presents an edge segment based moving object segmentation algorithm independent of background model. The proposed method detects moving edges using three most recent frames where moving regions are extracted by employing a watershed based algorithm. Extracted moving segments help to ensure good visual quality even in limited bit rate multimedia communications by incorporating priority basedtransmission.</p
A flexible edge matching technique for object detection in dynamic environment
No full textConsidering the robustness, stability and reduced volume of data, researchers have focused on using edge information in various video processing applications including moving object detection, tracking and target recognition. Though the edge information is more robust compared to intensity, it also exhibits variations in different frames due to illumination change and noise. In addition to this, the amount of variation varies from edge to edge. Thus, without making use of this variability information, it is difficult to obtain an optimal performance during edge matching. However, traditional edge pixel-based methods do not keep structural information of edges and thus they are not suitable to extract and hold this variability information. To achieve this, we represent edges as segments that make use of the structural and relational information of edges to allow extraction of this variability information. During edge matching, existing algorithms do not handle the size, positional and rotational variations to deal with edges of arbitrary shapes. In this paper, we propose a knowledge-based flexible edge matching algorithm where knowledge is obtained from the statistics on the environmental dynamics, and flexibility is to deal with the arbitrary shape and the geometric variations of edges by making use of this knowledge. In this paper, we detailed the effectiveness of the proposed matching algorithm in moving object detection and also indicated its suitability in other applications like target detection and tracking.</p
Background independent moving object segmentation using edge similarity measure
No full textBackground modeling is one of the most challenging and time consuming tasks in moving object detection for video surveillance. In this paper, we present a new algorithm which does not require any background model. Instead, it utilizes three most recent consecutive frames to detect the presence of moving object by extracting moving edges. In the proposed method, we introduce an edge segment based approach instead of traditional edge pixel based approach. We also utilize an efficient edge-matching algorithm which reduces the variation of edge localization in different frames. Finally, regions of the moving objects are extracted from previously detected moving edges by using an efficient watershed based segmentation algorithm. The proposed method is characterized through robustness against the random noise, illumination variations and quantization error and is validated with the extensive experimental results.</p
Background Independent Moving Object Segmentation for Video Surveillance
No full textBackground modeling is one of the most challenging and time consuming tasks in motion detection from video sequence. This paper presents a background independent moving object segmentation algorithm utilizing the spatio-temporal information of the last three frames. Existing three-frame based methods face challenges due to the insignificant gradient information in the overlapping region of difference images and edge localization errors. These methods extract scattered moving edges and experience poor detection rate especially when objects with slow movement exist in the scene. Moreover, they are not much suitable for moving object segmentation and tracking. The proposed method solves these problems by representing edges as segments and applying a novel segment based flexible edge matching algorithm which makes use of gradient accumulation through distance transformation. Due to working with three most recent frames, the proposed method can adapt to changes in the environment. Segment based representation facilitates local geometric transformation and thus it can make proper use of flexible matching to provide an effective solution for tracking. To segment the moving object region from the detected moving edges, we introduce a watershed based algorithm followed by an iterative background removal procedure. Watershed based segmentation algorithm helps to extract moving object with more accurate boundary which eventually achieves higher coding efficiency in content based applications and ensures a good visual quality even in the limited bit rate multimedia communication
Moving object detection and classification using neural network
No full textMoving object detection and classification is an essential and emerging research issue in video surveillance, mobile robot navigation and intelligent home networking using distributed agents. In this paper, we present a new approach for automatic detection and classification of moving objects in a video sequence. Detection of moving edges does not require background; only three most recent consecutive frames are utilized. We employ a novel edge segment based approach along with an efficient edge-matching algorithm based on integer distance transformation, which is efficient considering both accuracy and time together. Being independent of background, the proposed method is faster and adaptive to the change of environment. Detected moving edges are utilized to classify moving object by using neural network. Experimental results, presented in this paper demonstrate the robustness of proposed method.</p
An Adaptive Motion Segmentation for Automated Video Surveillance
No full textThis paper presents an adaptive motion segmentation algorithm utilizing spatiotemporal information of three most recent frames. The algorithm initially extracts the moving edges applying a novel flexible edge matching technique which makes use of a combined distance transformation image. Then watershed-based iterative algorithm is employed to segment the moving object region from the extracted moving edges. The challenges of existing three-frame-based methods include slow movement, edge localization error, minor movement of camera, and homogeneity of background and foreground region. The proposed method represents edges as segments and uses a flexible edge matching algorithm to deal with edge localization error and minor movement of camera. The combined distance transformation image works in favor of accumulating gradient information of overlapping region which effectively improves the sensitivity to slow movement. The segmentation algorithm uses watershed, gradient information of difference image, and extracted moving edges. It helps to segment moving object region with more accurate boundary even some part of the moving edges cannot be detected due to region homogeneity or other reasons during the detection step. Experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of the proposed method.</p
Contaminated ECG Artifact Detection and Elimination from EEG Using Energy Function Based Transformation
No full textElectrical field of the heart (ECG) propagates throughout the body and introduce artifact in EEG recordings which may lead to incorrect interpretation of monitoring result. Hence in this paper, we present a method of automatic detection and reduction of ECG artifact from EEG ECG has its own spike like property and periodicity. Moreover, it also has lack of correlation with the EEG signal. We have utilized the aforementioned properties to detect ECG artifact in EEG and have employed a method to remove it automatically. In the first step of the algorithm, an energy function based method is used to emphasize the R-waves of contaminated ECG artifact and thereafter, an adaptive thresholding method along with clustering is used to detect contaminated candidate R-spikes of ECG artifact in EEG signal. After that utilizing periodic information of R-wave, a searching mechanism is employed as post processing to detect the R-peaks more accurately. Thereafter, noise model of ECG artifact contaminated with EEG is generated and finally it is subtracted from the EEG recordings to decontaminate it from the artifact. Before subtraction, a time varying alignment procedure is applied to increase the effectiveness of the artifact reduction method. Results obtained from our extensive experiments show that the proposed method is effective and encouraging in terms of automatic ECG artifact detection and reduction from EEG signal.</p
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