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The problem of object detection and tracking has received relatively less attention in low frame rate and low resolution videos. Here we focus on motion segmentation in videos where objects appear small (less than 30-pixel tall people) and have low frame rate (less than 5 Hz). We study challenging cases where some of the, otherwise successful, approaches may break down. We investigate a number of...
This paper presents a novel approach for moving object segmentation in the H.264/AVC compressed domain, which based on ant colony clustering algorithm. Firstly, the motion vector (MV) field is extracted from the H.264/AVC compressed video, and then merges motion vectors with the same characteristic. Secondly, an improved ant colony clustering algorithm is used to classify the MV field into different...
A video sequence consists of several hundred frames and as a result creating a panoramic image from these frames is a very time consuming process. Consecutive frames have large overlap areas which do not provide much information. Therefore, some key frames must be extracted for better performance. There are a number of methods for key frame selection, which match all frames in a video sequence. In...
In this paper, a novel method for extracting moving objects from video sequences, which is based on Gaussian mixture model and watershed, is proposed. In order to overcome the drawback of subjective fixed threshold of traditional temporal segmentation, the difference image is modeled as a mixture of Gaussian distributions and a novel method to decide the model size and initial parameters of GMM is...
Motion segmentation is a very critical task in video surveillance system. In this paper, we propose a novel approach to detect moving objects in a complex background. Gaussian mixture model (GMM) is an effective way to extract moving objects from a video sequence. However, the conventional mixture Gaussian method suffers from false motion detection in complex backgrounds and slow convergence. This...
Motion analysis is an important component of surveillance, video annotation and many other applications. Current work focuses on the tracking of moving entities, the representation of their actions and the classification of sequences. A wide range of methods are available for the characterization and analysis of human activity. This work presents an original approach for the detailed characterization...
Segmentation of semantic video object planes (VOPpsilas) from video sequence is a key to the standard MPEG-4 with content-based video coding. In our paper, we propose an automatic segmentation algorithm under static background. The algorithm can extract accurate video objects from slow moving video sequences. The initial two coarse masks are first obtained based on frame difference and motion detection,...
The capability of extracting moving objects from a video sequence captured using a static camera is a typical first step in visual surveillance. This procedure is called a background subtraction (BGS), and it uses the temporal distribution of pixel values over the sequence of frames. Pixel based BGS can be improved by considering the spatial coherence around each pixel, and in this paper we present...
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