  In this paper, a robust visual tracking approach via mixed model based
convolutional neural networks (SDT) is developed. In order to handle abrupt or
fast motion, a prior map is generated to facilitate the localization of region
of interest (ROI) before the deep tracker is performed. A top-down saliency
model with nineteen shallow cues are employed to construct the prior map with
online learnt combination weights. Moreover, apart from a holistic deep
learner, four local networks are also trained to learn different components of
the target. The generated four local heat maps will facilitate to rectify the
holistic map by eliminating the distracters to avoid drifting. Furthermore, to
guarantee the instance for online update of high quality, a prioritised update
strategy is implemented by casting the problem into a label noise problem. The
selection probability is designed by considering both confidence values and
bio-inspired memory for temporal information integration. Experiments are
conducted qualitatively and quantitatively on a set of challenging image
sequences. Comparative study demonstrates that the proposed algorithm
outperforms other state-of-the-art methods.
