Robust single target tracking using determinantal point process observations

The efficiency and robustness of modern visual tracking systems are largely dependent on the object detection system at hand. Bernoulli and Multi-Bernoulli filters have been proposed for visual tracking without explicit detections (image observations). However, these previous approaches do not fully exploit discriminative features for tracking. In this paper, we propose a novel Bernoulli filter with determinantal point processes observations. The proposed observation model can select groups of detections with high detection scores and low correlation among the observed features; thus achieving a robust filter.