Labeled multi-Bernoulli track-before-detect for multi-target tracking in video

This paper presents a labeled multi-Bernoulli filter for track-before-detect with a special focus on visual tracking of multiple targets in video. We show that labeled multi-Bernoulli distribution is a conjugate prior for an image likelihood function with a specific separable form. Following a previously formulated likelihood function (with the desirable separable form) using background subtraction, we apply our proposed labeled multi-Bernoulli filter. Our simulation results show that the proposed solution can successfully track multiple targets in a public visual tracking dataset. Comparative results show superior tracking performance compared with recent competing methods.