Multi-object tracking using an adaptive transition model particle filter with region covariance data association

We present an approach for detection, labelling and tracking multiple objects through both temporally and spatially significant occlusions. The proposed method builds on the idea of multiple objects scenario where grouping and occlusions are a reality. To this end, the objects are represented by covariance matrices and particle filters perform the object tracking. We propose a different measurement for the particles weights and a new update for the objects descriptor in a Riemannian framework. The results show the effectiveness of the approach hereby proposed in very clutter scenes.