International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 03 | Mar -2017
www.irjet.net
e-ISSN: 2395 -0056 p-ISSN: 2395-0072
ROBUST TRACKING VIA FEATURE MAPPING METHOD AND SUPPORT VECTOR MACHINE A.Akalya1, M.Fiona clen maurus2, V.Vinothini3, S.Selva Agnes4 123Student,Dept 4Assistant
Of ECE ,Panimalar Institute Of Technology,Tamilnadu,India.
professor,Dept Of ECE,Panimalar Institute Of Technology,Tamilnadu,India.
--------------------------------------------------------------------------------------------------------------------------------------------------------Abstract: Visual tracking is a challenging process due to variations caused by various factors such as object deformation, occlusion, scale and illumination changes. In our proposed system, we tend to overcome these drawbacks by using expectation maximization algorithm and support vector machine. By using this algorithm, we can improve the accuracy while tracking of object or a person from a video. This tracking model is better in terms of efficiency and robustness. This tracker maintains a speed of approximately 45frames/sec. KEYWORDS: Expectation Tracking,Accuracy.
maximization,
Support
Vector
machine,
Positivetemplates,occlusion
detection,
1.INTRODUCTION: Visual Object tracking is a fundamental problem in image processing. It has various applications like motion analysis, video surveillance, human computer interaction and robot perception. Although there are many researches going on for the development of this process, it is still challenging due to some factors like appearance, pose change, occlusion etc. Hence it is necessary to develop better feature representation to achieve more effective tracking models. The intuition behind SFA is linked to the assumption that the information contained in a signal changes not suddenly, but slowly. Note, a signal generally contains high variation (caused by noise), nonetheless, it is the seldom varying features that mark the separation between informative changes. SFA extracts these features, as it selects the important attributes which change least over time. 2.BLOCK DIAGRAM:
Input video to frame conversion
Gray image
Feature extracti on
Shift competing
learning
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