Cascaded Tracking with Incrementally Learned Projections

A convention in visual object tracking is to only favor the candidate with maximum similarity score and take it as the tracking result, while ignore the rest. However, surrounded samples also provide valuable information for target locating, and the combination of their votes can produce more stable results. In this paper, we have proposed a novel method based on the supervised descent method (SDM). We search for the target from multiple start positions and locate it with their votes. For evaluating each predicted descent direction, we have presented a confidence estimating scheme for SDM. To adapt the tracking model to appearance variations, we have further presented an incremental cascaded support vector regression (ICSVR) algorithm for model updating. Experimental results on a recent benchmark demonstrate the superior performance of our tracker against state-of-the-arts.