  Object trackers based on Convolution Neural Network (CNN) have achieved
state-of-the-art performance on recent tracking benchmarks, while they suffer
from slow computational speed. The high computational load arises from the
extraction of the feature maps of the candidate and training patches in every
video frame. The candidate and training patches are typically placed randomly
around the previous target location and the estimated target location
respectively. In this paper, we propose novel schemes to speed-up the
processing of the CNN-based trackers. We input the whole region-of-interest
once to the CNN to eliminate the redundant computations of the random candidate
patches. In addition to classifying each candidate patch as an object or
background, we adapt the CNN to classify the target location inside the object
patches as a coarse localization step, and we employ bilinear interpolation for
the CNN feature maps as a fine localization step. Moreover, bilinear
interpolation is exploited to generate CNN feature maps of the training patches
without actually forwarding the training patches through the network which
achieves a significant reduction of the required computations. Our tracker does
not rely on offline video training. It achieves competitive performance results
on the OTB benchmark with 8x speed improvements compared to the equivalent
tracker.
