Real-time monocular people tracking by sequential Monte-Carlo filtering

Abstract

We present a solution to the people tracking problem using a monocular vision approach from a bird's eye view and Sequential Monte-Carlo Filtering. Each tracked human is represented by an individual Particle Filter using spheroids as a three-dimensional approximation to the shape of the upstanding human body. We use the bearings-only model as the state update function for the particles. Our measurement likelihood function to estimate the probability of each particle is imitating the image formation process. This involves also partial occlusion by dynamic movements from other humans within neighbored areas. Due to algorithmic optimization the system is real-time capable and therefore not only limited to surveillance or human motion analysis. It could rather be used for Human-Computer-Interaction (HCI) and indoor location. To demonstrate this capabilities we evaluated the accuracy of the system and show the robustness in different levels of difficulty.