  In this paper, we present a simple yet effective Boolean map based
representation that exploits connectivity cues for visual tracking. We describe
a target object with histogram of oriented gradients and raw color features, of
which each one is characterized by a set of Boolean maps generated by uniformly
thresholding their values. The Boolean maps effectively encode multi-scale
connectivity cues of the target with different granularities. The fine-grained
Boolean maps capture spatially structural details that are effective for
precise target localization while the coarse-grained ones encode global shape
information that are robust to large target appearance variations. Finally, all
the Boolean maps form together a robust representation that can be approximated
by an explicit feature map of the intersection kernel, which is fed into a
logistic regression classifier with online update, and the target location is
estimated within a particle filter framework. The proposed representation
scheme is computationally efficient and facilitates achieving favorable
performance in terms of accuracy and robustness against the state-of-the-art
tracking methods on a large benchmark dataset of 50 image sequences.
