Abstract

Tracking multiple ob jects is important in many application domains. We propose a novel algorithm for multi-ob ject tracking that is capable of working under very challenging conditions such as min- imal hardware equipment, uncalibrated monocular camera, occlusions and severe background clutter. To address this problem we propose a new method that jointly estimates ob ject tracks, estimates correspond- ing 2D/3D temporal tra jectories in the camera reference system as well as estimates the model parameters (pose, focal length, etc) within a coherent probabilistic formulation. Since our goal is to estimate stable and robust tracks that can be univocally associated to the ob ject IDs, we propose to include in our formulation an interaction (attraction and repulsion) model that is able to model multiple 2D/3D tra jectories in space-time and handle situations where ob jects occlude each other. We use a MCMC particle filtering algorithm for parameter inference and propose a solution that enables accurate and efficient tracking and cam- era model estimation. Qualitative and quantitative experimental results obtained using our own dataset and the publicly available ETH dataset shows very promising tracking and camera estimation results.