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.