Robust object tracking with bidirectional corner matching and trajectory smoothness algorithm
This paper proposes a novel method for robust object tracking. The method consists of three different components: a short term tracker, an object detector, and an online object model. For the short term tracker, we use an advanced Lucas Kanade tracker with bidirectional corner matching to capture object frame by frame. Meanwhile, statistical filtering and matching algorithm combined with haar-like feature random fern play as a detector to extract all possible object candidates in the current frame. Making use of trajectory information, the online object model decides the best target match among the candidates. And the model also trains the random fern feature adaptively online to better guide consecutive tracking. We demonstrate our method is robust to track an object in a long term and under large variations of view angle and lighting conditions. Moreover, our method is efficient to re-detect the object and keep tracking even after it's out of view or recover from heavy occlusion. To achieve state-of-the-art performance, it is highlighted that our method can be extended to multiple objects tracking application. Finally, comparisons with other state-of-the-art trackers are presented to show the robustness of our tracker.