Modified Particle Filtering using Foreground Separation and Confidence for Object Tracking

Title
Modified Particle Filtering using Foreground Separation and Confidence for Object Tracking
Authors
김찬수박성기
Keywords
vision-based; human tracking; particle filtering; tracking by detection
Issue Date
2015-08
Publisher
IEEE International Conference on Advanced Video-and Signal-based surveillance (AVSS)
Citation
, 1-6
Abstract
Particle filter is a widely used framework for object tracking, but it is vulnerable when its observation model is based on visual appearance. In this paper, we propose a modified particle filtering that makes use of foreground regions and their pixel-based confidences that are likely to be foreground; the foreground regions are used for preventing generations of particle in the background and the pixel-based confidences are enable to enhance the similarity between foreground and observation models. We evaluate the performance on five datasets and show that the proposed approach outperforms a number of state-of-the-art object tracking methods.
URI
http://pubs.kist.re.kr/handle/201004/51238
Appears in Collections:
KIST Publication > Conference Paper
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