Efficient tracking with distinctive target colors and silhouette

Target tracking using color based appearance models is very popular in visual tracking. However, trackers based only on color are fragile and often drift to the background when it has similar appearances. In this paper, we propose an efficient way to use distinctive target colors to track the target and eliminate the drift problem. Colors are sampled from the target and its immediate surrounding region. And color samples coming from target result in more distinctive target color. In our approach, we use a short and a long time color histogram to represent the target color. The short time color histogram is used to calculate the distinctiveness of colors while the long time color histogram is used to keep the target color that is consistent over time. In our approach, the target is not marked as a rectangle or other geometric primitives, instead, we track it with its own silhouette. Using silhouette to mark target significantly reduces the false positive information during online learning. Also, the color models are updated with a dynamic learning factor which is based on the tracking result. After testing with many tracking sequences and comparison with other state-of-art trackers, the proposed tracking algorithm shows comparably better performance with very high tracking rate.