3,387 research outputs found
Vision-Based Production of Personalized Video
In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitorâs stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
Interaction between high-level and low-level image analysis for semantic video object extraction
Authors of articles published in EURASIP Journal on Advances in Signal Processing are the copyright holders of their articles and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article, according to the SpringerOpen copyright and license agreement (http://www.springeropen.com/authors/license)
Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices
In this paper, an automatic method is proposed to perform image registration
in visible and infrared pair of video sequences for multiple targets. In
multimodal image analysis like image fusion systems, color and IR sensors are
placed close to each other and capture a same scene simultaneously, but the
videos are not properly aligned by default because of different fields of view,
image capturing information, working principle and other camera specifications.
Because the scenes are usually not planar, alignment needs to be performed
continuously by extracting relevant common information. In this paper, we
approximate the shape of the targets by polygons and use affine transformation
for aligning the two video sequences. After background subtraction, keypoints
on the contour of the foreground blobs are detected using DCE (Discrete Curve
Evolution)technique. These keypoints are then described by the local shape at
each point of the obtained polygon. The keypoints are matched based on the
convexity of polygon's vertices and Euclidean distance between them. Only good
matches for each local shape polygon in a frame, are kept. To achieve a global
affine transformation that maximises the overlapping of infrared and visible
foreground pixels, the matched keypoints of each local shape polygon are stored
temporally in a buffer for a few number of frames. The matrix is evaluated at
each frame using the temporal buffer and the best matrix is selected, based on
an overlapping ratio criterion. Our experimental results demonstrate that this
method can provide highly accurate registered images and that we outperform a
previous related method
- âŠ