Accession Number:

ADA564734

Title:

Robust Infrared Vehicle Tracking across Target Pose Change using L1 Regularization

Descriptive Note:

Conference paper

Corporate Author:

TEMPLE UNIV PHILADELPHIA PA DEPT OF COMPUTER AND INFORMATION SCIENCES

Personal Author(s):

• Ling, Haibin
• Bai, Li
• Blasch, Erik
• Mei, Xue

Report Date:

2010-07-01

Pagination or Media Count:

9.0

Abstract:

In this paper, we propose a robust vehicle tracker for Infrared IR videos motivated by the recent advance in compressive sensing CS. The new eL1-PF tracker solves a sparse model representation of moving targets via L1 regularized least squares. The sparse-model solution addresses real-world environmental challenges such as image noises and partial occlusions. To further improve tracking performance for frame-to-frame sequences involving large target pose changes two extensions to the original L1 tracker are introduced eL1. First, in the particle filter PF framework, pose information is explicitly modelled into the state space which significantly improves the effectiveness of particle sampling and propagation. Second, a probabilistic template update scheme is designed, which helps alleviating drift caused by a target pose change. The proposed tracker, named eL1-PF tracker, is tested on IR sequences from the DARPA Video Verification of Identity VIVID dataset. Promising results from the eL1-PF tracker are observed in these experiments in comparison with previous mean-shift and original L1--regularization trackers.

Descriptors:

• *INFRARED TRACKING
• *TARGETS
• *VEHICLES
• ADAPTERS
• COMPARISON
• DRIFT
• ENVIRONMENTS
• FILTERS
• IMAGES
• INFRARED RADIATION
• LEAST SQUARES METHOD
• MODELS
• NOISE
• PARTICLES
• PROBABILITY
• PROPAGATION
• SEQUENCES
• SYMPOSIA
• TEMPLATES
• TRACKING
• VERIFICATION
• VIDEO SIGNALS

Subject Categories:

• Infrared Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE