In order to study the problem of long-term visual tracking in unconstrained environments

this paper proposes a method of learning multiple detectors online for visual object tracking.The method uses the random ferns as the basic detector.The entire and the local appearances of the target and the connected objects which are explored by the context learning are used synchronously as the training data to build and upgrade the object detector on the fly.Thus it is able to detect the objects with different classes independently.Since different detections are related to different object classes

the results of object detections are fused as the measurements and the probabilities of configuration hypotheses for the measurements to the target are calculated to find the target location for visual tracking task.Experimental results based on the real-world video sequences validate the effectiveness and robustness of our approach and demonstrate its better tracking performance than several state-of-the-art methods.