Autonomous detection and tracking under illumination changes, occlusions and moving camera

作者：Bhaskar Harish^*; Dwivedi Kartik; Dogra Debi Prosad; Al Mualla Mohammed; Mihaylova Lyudmila

来源：Signal Processing, 2015, 117: 343-354.

DOI：10.1016/j.sigpro.2015.06.003

• Target detection
• Tracking
• Illumination variations
• Occlusion
• Camera movements
• Reverse analysis
• Rao-Blackwellized particle filter
• Likelihood model

In this paper, an autonomous multiple target detection and tracking technique for dynamic scenes that are influenced by illumination variations, occlusions and camera instability is proposed. The framework combines a novel Dynamic Reverse Analysis (DRA) approach with an Enhanced Rao-Blackwellized Particle Filter (E-RBPF) for multiple target detection and tracking, respectively. The DRA method, in addition to providing accurate target localization, presents the E-RBPF scheme with costs associated with the differences in intensity caused by illumination variations between consecutive frame pairs in any video of a dynamic scene. The E-RBPF inherently models these costs, thus allowing the framework to (a) adapt learning parameters, (b) distinguish between camera-motion and object-motion, (c) deal with sample degeneracy, (d) provide appropriate appearance compensation during likelihood measurement and (e) handle occlusion. The proposed detect-and-track method when compared against other competing baseline techniques has demonstrated superior performance both in accuracy and robustness on challenging videos from publicly available datasets.

• 出版日期2015-12