Conference Paper

A Theoretic Framework for Object Class Tracking

California State Univ. at Fresno, Fresno
DOI: 10.1109/ICNSC.2008.4525430 Conference: Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on
Source: IEEE Xplore


Suppose we have a video, the first half of this video is capturing the images of a sedan and the second half is recording the moving of a truck, can we use the same video tracking algorithm to follow the moving of the object over the entire video sequence? We define this kind of problem as "object class tracking" problem. Instead of tracking a specific object, object class tracking is to track the moving of the object class. The challenge is how to locate the image element in the next frame by handling the large intra-class variance. In this paper, we propose a theoretic framework for object class tracking based on Kalman filter. A part-based statistical model is employed to solve the image element localization problem. We mathematically prove the soundness of the theoretic framework. The method has the potential to be applied in many application domains.

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    • "For (i), tracking algorithms are expected to quickly identify the location of objects, predict the objects in the sequential frames, and handle the transformation and occlusion. Our system implements Kalman Filter algorithm to track the moving of an object class [10]. For (ii), because most multimedia data are of huge size, segmenting them into smaller and meaningful chunks may help improve throughput or other related QoS. "
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    ABSTRACT: Developing a large-scale biomedical multimedia software system is always a challenging task: Satisfaction of sundry and stringent biomedical multimedia related requirements and standards; Heterogeneous software deployment and communication environments; and tangling correlation between data/contents and software functionalities, among others. This paper introduces a novel biomedical multimedia software system developed under Service-Oriented Architecture (SOA). Such a system takes the advantage of interoperability of SOA to solve the heterogeneity and correlation problems. The paper also classifies the system into services, annotation, ontologies, semantics matching, and QoS optimization aspects which may potentially solve the requirements problem: By establishing data ontology with respect to data properties, contents, QoS, and biomedical regulations and expanding service ontology to describe more functional and QoS specifications supported by services, appropriate services for processing biomedical multimedia data may be discovered, performed, tuned up or replaced as needed. Lastly, a biomedical education project that improves the performance of feature extraction and classification processed afterwards is introduced to illustrate the advantages of our software system developed under SOA.
    Full-text · Article · Aug 2010 · Journal of multimedia