Real-time object tracking based on Hough ferns

In order to deal with the tough problem of providing high accuracy and meanwhile achieving real-time tracking using Hough-based approaches under very limited samples for training, a Hough ferns based method was proposed for object tracking. This method uses the random ferns as the basic detector. It samples the local appearances of object as training set, and computes and saves the Hough votes for each leaf-node. The detector and object model were learned online at runtime to adapt to the variation of object and the TLD (tracking-learning-detection) was improved to achieve long-term visual tracking in unconstrained environment. Experimental results on Babenko sequences demonstrate that the average running speed of the tracker based on the proposed approach on a normal PC is 3fps and the average accuracy rate is 87.1%, showing its better tracking performance than several state-of-the-art methods.