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In this paper, we propose a novel geospatial image and video filtering tool (GIFT) to select the most relevant input images and videos for computer vision applications with geotagged mobile videos. GIFT tightly couples mobile media content and their geospatial metadata for fine granularity video manipulation and intelligently indexes FOVs (Field of View) to deal with large volumes of data. To demonstrate...
Reliable object tracking in complex visual environment is a challenging problem in the field of computer vision. One of the common problems in object tracking is partial and full object occlusions. And especially in the condition of long- lived full occlusion during which the full occlusion lasts for tens of frames, the tracking is more difficult. This paper proposes an occlusion handling scheme based...
SIFT is one of the most robust feature extraction algorithms and widely used in object tracking field. While invariant to scale, rotation and other image transforms, the traditional SIFT algorithm is rather time-consuming in creating description of the large number of keypoints. Aiming at reducing tracking time and complexity to achieve real time compliance, this paper proposes Homography Matrix based...
Object tracking plays an important role in video surveillance system. However, in the field of object tracking, complex object motion and object occlusions still remains challenging topics. This paper proposes a Estimation-Correction (EC) object tracking scheme in real scenarios, combining the strength of scale invariant feature transform (SIFT) and mean shift algorithm. The corresponding SIFT features...
Moving object tracking has received much interest in the field of computer vision due to the increasing need for automated video analysis. Particles Filter is a very promising object tracking method since it is suitable for non-linear and/or non-Gaussian applications. Most particle filter applies color information in target model which might fail in the presence of similar colored objects in the scene...
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