  Multi-People Tracking in an open-world setting requires a special effort in
precise detection. Moreover, temporal continuity in the detection phase gains
more importance when scene cluttering introduces the challenging problems of
occluded targets. For the purpose, we propose a deep network architecture that
jointly extracts people body parts and associates them across short temporal
spans. Our model explicitly deals with occluded body parts, by hallucinating
plausible solutions of not visible joints. We propose a new end-to-end
architecture composed by four branches (visible heatmaps, occluded heatmaps,
part affinity fields and temporal affinity fields) fed by a time linker feature
extractor. To overcome the lack of surveillance data with tracking, body part
and occlusion annotations we created the vastest Computer Graphics dataset for
people tracking in urban scenarios by exploiting a photorealistic videogame. It
is up to now the vastest dataset (about 500.000 frames, almost 10 million body
poses) of human body parts for people tracking in urban scenarios. Our
architecture trained on virtual data exhibits good generalization capabilities
also on public real tracking benchmarks, when image resolution and sharpness
are high enough, producing reliable tracklets useful for further batch data
association or re-id modules.
