Non-overlapping Distributed Tracking System Utilizing Particle Filter
Tracking people across multiple cameras is a challenging research area in visual computing, especially when these cameras have non-overlapping field of views. The important task is to associate a current subject with other prior appearances of the same subject across time and space in a camera netwo...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Springer Netherlands
2007
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| Online Access: | http://hdl.handle.net/20.500.11937/41093 |
| _version_ | 1848756048621469696 |
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| author | Lim, Fee Lee Leoputra, Wilson Tan, Tele |
| author_facet | Lim, Fee Lee Leoputra, Wilson Tan, Tele |
| author_sort | Lim, Fee Lee |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Tracking people across multiple cameras is a challenging research area in visual computing, especially when these cameras have non-overlapping field of views. The important task is to associate a current subject with other prior appearances of the same subject across time and space in a camera network. Several known techniques rely on Bayesian approaches to perform the matching task. However, these approaches do not scale well when the dimension of the problem increases; e.g. when the number of subject or possible path increases. The aim of this paper is to propose a unified tracking framework using particle filters to efficiently switch between visual tracking (field of view tracking) and track prediction (non-overlapping region tracking). The particle filter tracking system utilizes a map (known environment) to assist the tracking process when targets leave the field of view of any camera. We implemented and tested this tracking approach in an in-house multiple cameras system as well as using on-line data. Promising results were obtained which suggested the feasibility of such an approach. |
| first_indexed | 2025-11-14T09:06:00Z |
| format | Journal Article |
| id | curtin-20.500.11937-41093 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:06:00Z |
| publishDate | 2007 |
| publisher | Springer Netherlands |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-410932017-09-13T14:28:47Z Non-overlapping Distributed Tracking System Utilizing Particle Filter Lim, Fee Lee Leoputra, Wilson Tan, Tele Tracking people across multiple cameras is a challenging research area in visual computing, especially when these cameras have non-overlapping field of views. The important task is to associate a current subject with other prior appearances of the same subject across time and space in a camera network. Several known techniques rely on Bayesian approaches to perform the matching task. However, these approaches do not scale well when the dimension of the problem increases; e.g. when the number of subject or possible path increases. The aim of this paper is to propose a unified tracking framework using particle filters to efficiently switch between visual tracking (field of view tracking) and track prediction (non-overlapping region tracking). The particle filter tracking system utilizes a map (known environment) to assist the tracking process when targets leave the field of view of any camera. We implemented and tested this tracking approach in an in-house multiple cameras system as well as using on-line data. Promising results were obtained which suggested the feasibility of such an approach. 2007 Journal Article http://hdl.handle.net/20.500.11937/41093 10.1007/s11265-007-0091-4 Springer Netherlands fulltext |
| spellingShingle | Lim, Fee Lee Leoputra, Wilson Tan, Tele Non-overlapping Distributed Tracking System Utilizing Particle Filter |
| title | Non-overlapping Distributed Tracking System Utilizing Particle Filter |
| title_full | Non-overlapping Distributed Tracking System Utilizing Particle Filter |
| title_fullStr | Non-overlapping Distributed Tracking System Utilizing Particle Filter |
| title_full_unstemmed | Non-overlapping Distributed Tracking System Utilizing Particle Filter |
| title_short | Non-overlapping Distributed Tracking System Utilizing Particle Filter |
| title_sort | non-overlapping distributed tracking system utilizing particle filter |
| url | http://hdl.handle.net/20.500.11937/41093 |