Hierarchical data association and depth-invariant appearance model for indoor multiple objects tracking

Discriminative target representation is vital for data association in multi-tracking. In order to increase the discriminative power, pervious works always combine bunch of features for target representation. However, this is prone to error accumulation and unnecessary computational cost, which may increase identity switches in data association on the contrary. To address this problem, we propose a hierarchical data association scheme which gradually combines features to the minimum requirements of discriminating ambiguous targets. In addition, indoor multi-tracking is more challenging due to frequent occlusion, view-truncation, large scale and pose variation, which may bring considerable unreliability for target representation. To handle this a novel depth-invariant part-based appearance model using RGB-D data is proposed. The depth-invariant appearance have stable length metric proportional to the absolute length metric in the world coordinates, which increase its robustness to scale variation. The part-based nature makes it robust to partial occlusion and view-truncation. Our algorithm is validated on various challenging indoor environments and it demonstrates high processing speed up to 50 fps and competitive accuracy.