1 November 2011 Tracking a large number of closely spaced objects based on the particle probability hypothesis density filter via optical sensor
Liangkui Lin, Hui Xu, Wei An, Weidong Sheng, Dan Xu
Author Affiliations +
Abstract
This paper presents a novel approach to tracking a large number of closely spaced objects (CSO) in image sequences that is based on the particle probability hypothesis density (PHD) filter and multiassignment data association. First, the particle PHD filter is adopted to eliminate most of the clutters and to estimate multitarget states. In the particle PHD filter, a noniterative multitarget estimation technique is introduced to reliably estimate multitarget states, and an improved birth particle sampling scheme is present to effectively acquire targets among clutters. Then, an integrated track management method is proposed to realize multitarget track continuity. The core of the track management is the track-to-estimation multiassignment association, which relaxes the traditional one-to-one data association restriction due to the unresolved focal plane CSO measurements. Meanwhile, a unified technique of multiple consecutive misses for track deletion is used jointly to cope with the sensitivity of the PHD filter to the missed detections and to eliminate false alarms further, as well as to initiate tracks of large numbers of CSO. Finally, results of two simulations and one experiment show that the proposed approach is feasible and efficient.
©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Liangkui Lin, Hui Xu, Wei An, Weidong Sheng, and Dan Xu "Tracking a large number of closely spaced objects based on the particle probability hypothesis density filter via optical sensor," Optical Engineering 50(11), 116401 (1 November 2011). https://doi.org/10.1117/1.3651798
Published: 1 November 2011
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Particles

Particle filters

Electronic filtering

Optical tracking

Staring arrays

Image filtering

Filtering (signal processing)

Back to Top