Online Visual Tracking Using Temporally Coherent Part Clusters

Recent advances in visual tracking have focused on handling deformations and occlusions using the part-based appearance model. However, it remains a challenge to come up with a reliable target representation using local parts, and hence existing trackers continue to face drifting problems. To deal with this challenge, we propose a robust online model, formulating the tracking task as a problem of identifying Temporally Coherent Part (TCP) clusters. Specifically, we pose the TCP clusters identification task as a dense neighborhoods searching problem using a relational hypergraph in which the relationship among multiple temporal local parts is encoded as the affinity value of a hyperedge connecting them. Such high-order relationships among multiple local parts across the temporal domain make our tracker more robust towards deformations and occlusions. Extensive experiments on various challenging video sequences demonstrate that our TCP-based method performs better than the state-of-the-art methods.