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

• Analysis of illumination changes in a scene via forward & reverse background modeling..
• Generating sufficient statistics to characterize variations in illumination.
• Building a hybrid model of the background using selected frames.
• Optimized implementation of reverse analysis for trade-off between accuracy and load.
• TTightly integrated likelihood and noise models for robust Rao-Blackwellised Particle filtering.

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.