A New Scale and Orientation Adaptive Object Tracking System Using Kalman Filter and Expected Likelihood Kernel
| Autor: | Leila Essannouni, Fedwa Essannouni, Driss Aboutajdine, Hamd Ait Abdelali |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Kalman filter Extended Kalman filter Variable kernel density estimation Video tracking Histogram Kernel adaptive filter Ensemble Kalman filter Fast Kalman filter Computer vision Artificial intelligence business |
| Zdroj: | Lecture Notes in Electrical Engineering ISBN: 9783319302997 |
| DOI: | 10.1007/978-3-319-30301-7_13 |
| Popis: | This paper presents a new scale and orientation adaptive object tracking system using Kalman filter in a video sequence. This object tracking is an important task in many vision applications. The main steps in video analysis are two: detection of interesting moving objects and tracking of such objects from frame to frame. We use an efficient local search scheme (based on expected likelihood kernel) to find the image region with a histogram most similar to the histogram of the tracked object. In this paper, we address the problem of scale adaptation. The proposed approach tracker with scale selection is compared with recent state-of-the-art algorithms. Experimental results have been presented to show the effectiveness of our proposed system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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