Modified Particle Filtering using Foreground Separation and Confidence for Object Tracking
- Modified Particle Filtering using Foreground Separation and Confidence for Object Tracking
- 김찬수; 박성기
- vision-based; human tracking; particle filtering; tracking by detection
- Issue Date
- IEEE International Conference on Advanced Video-and Signal-based surveillance (AVSS)
- , 1-6
- 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.
- Appears in Collections:
- KIST Publication > Conference Paper
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.