Abstract

Although ‘tracking-by-detection’ is a popular approach when reliable ob ject detectors are available, missed detections remain a diffi- cult hurdle to overcome. We present a hybrid stochastic/deterministic optimization scheme that uses RJMCMC to perform stochastic search over the space of detection configurations, interleaved with deterministic computation of the optimal multi-frame data association for each pro- posed detection hypothesis. Since ob ject tra jectories do not need to be estimated directly by the sampler, our approach is more efficient than traditional MCMCDA techniques. Moreover, our holistic formulation is able to generate longer, more reliable tra jectories than baseline tracking- by-detection approaches in challenging multi-target scenarios.