  Object proposal generation methods have been widely applied to many computer
vision tasks. However, existing object proposal generation methods often suffer
from the problems of motion blur, low contrast, deformation, etc., when they
are applied to video related tasks. In this paper, we propose an effective and
highly accurate target-specific object proposal generation (TOPG) method, which
takes full advantage of the context information of a video to alleviate these
problems. Specifically, we propose to generate target-specific object proposals
by integrating the information of two important objectness cues: colors and
edges, which are complementary to each other for different challenging
environments in the process of generating object proposals. As a result, the
recall of the proposed TOPG method is significantly increased. Furthermore, we
propose an object proposal ranking strategy to increase the rank accuracy of
the generated object proposals. The proposed TOPG method has yielded
significant recall gain (about 20%-60% higher) compared with several
state-of-the-art object proposal methods on several challenging visual tracking
datasets. Then, we apply the proposed TOPG method to the task of visual
tracking and propose a TOPG-based tracker (called as TOPGT), where TOPG is used
as a sample selection strategy to select a small number of high-quality target
candidates from the generated object proposals. Since the object proposals
generated by the proposed TOPG cover many hard negative samples and positive
samples, these object proposals can not only be used for training an effective
classifier, but also be used as target candidates for visual tracking.
Experimental results show the superior performance of TOPGT for visual tracking
compared with several other state-of-the-art visual trackers (about 3%-11%
higher than the winner of the VOT2015 challenge in term of distance precision).
