  Visual representation is crucial for a visual tracking method's performances.
Conventionally, visual representations adopted in visual tracking rely on
hand-crafted computer vision descriptors. These descriptors were developed
generically without considering tracking-specific information. In this paper,
we propose to learn complex-valued invariant representations from tracked
sequential image patches, via strong temporal slowness constraint and stacked
convolutional autoencoders. The deep slow local representations are learned
offline on unlabeled data and transferred to the observational model of our
proposed tracker. The proposed observational model retains old training samples
to alleviate drift, and collect negative samples which are coherent with
target's motion pattern for better discriminative tracking. With the learned
representation and online training samples, a logistic regression classifier is
adopted to distinguish target from background, and retrained online to adapt to
appearance changes. Subsequently, the observational model is integrated into a
particle filter framework to peform visual tracking. Experimental results on
various challenging benchmark sequences demonstrate that the proposed tracker
performs favourably against several state-of-the-art trackers.
