Person tracking-by-detection with efficient selection of part-detectors

In this paper we introduce a new person tracking-by-detection approach based on a particle filter. We leverage detection and appearance cues and apply explicit occlusion reasoning. The approach samples efficiently from a large set of available person part-detectors in order to increase runtime performance while retaining accuracy. The tracking approach is evaluated and compared to the state of the art on the CAVIAR surveillance dataset as well as on a multimedia dataset consisting of six episodes of the TV series The Big Bang Theory. The results demonstrate the versatility of the approach on very different types of data and its robustness to camera movement and non-pedestrian body poses.