Online Learning of Robust Object Detectors During Unstable Tracking
Zdenek Kalal, Jiri Matas and Krystian Mikolajczyk
This work investigates the problem of robust, long-term visual tracking of unknown objects in unconstrained environments. It therefore must cope with frame-cuts, fast camera movements and partial/total object occlusions/dissapearances. We propose a new approach, called Tracking-Modeling-Detection (TMD) that closely integrates adaptive tracking with online learning of the object-specific detector. Starting from a single click in the first frame, TMD tracks the selected object by an adaptive tracker. The trajectory is observed by two processes (growing and pruning event) that robustly model the appearance and build an object detector on the fly. Both events make errors, the stability of the system is achieved by their cancelation. The learnt detector enables re-initialization of the tracker whenever previously observed appearance re-occurs. We show the real-time learning and classification is achievable with random forests. The performance and the long-term stability of TMD is demonstrated and evaluated on a set of challenging video sequences with various objects such as cars, people and animals.
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Zdenek Kálal|
|Deposited On:||17 June 2010|