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

In this paper,an autonomous multiple target detection and tracking technique fordynamic 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.Th 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 distinguishbetweencamera-motionand object-motion,(c)dealwithsampledegeneracy,(d)provideappropriateappearance compensationduringlikelihoodmeasurementand(e)handleocclusion.Theproposed detect-and-track methodwhencomparedagainstothercompetingbaselinetechniques has demonstratedsuperiorperformancebothinaccuracyandrobustnessonchallenging videos frompubliclyavailabledatasets.