Visual tracking based on edge field with object proposal association

In this paper, we present a novel tracking system based on edge-based object proposal and data association called object proposal association. Our object proposal method accurately detects and localizes objects in an image by searching for object-like regions, with the assumption that an object is represented by a closed boundary. To search for closed boundaries in an image, we present a new Edge Fields (EFs) technique. Using this technique, our method can extract high-quality edges and can obtain accurate boundaries from the image. The EFs technique consists of blurring and thresholding steps, where the former helps extract high-quality edges and the latter prevents the method from losing image details while blurring. After the method extracts object-like regions, we associate the regions in the previous frame with those in the current frame. For this purpose, using the Markov chain Monte Carlo data association (MCMCDA) algorithm, we can find pairs of similar regions across two frames. Experimental results demonstrate that our object proposal method is competitive with state-of-the-art object proposal methods on the PASCAL VOC 2007 dataset. Our tracking method is also competitive with state-of-the-art tracking methods on Object Tracking Benchmark dataset.