  This article presents a semantic tracker which simultaneously tracks a single
target and recognises its category. In general, it is hard to design a tracking
model suitable for all object categories, e.g., a rigid tracker for a car is
not suitable for a deformable gymnast. Category-based trackers usually achieve
superior tracking performance for the objects of that specific category, but
have difficulties being generalised. Therefore, we propose a novel unified
robust tracking framework which explicitly encodes both generic features and
category-based features. The tracker consists of a shared convolutional network
(NetS), which feeds into two parallel networks, NetC for classification and
NetT for tracking. NetS is pre-trained on ImageNet to serve as a generic
feature extractor across the different object categories for NetC and NetT.
NetC utilises those features within fully connected layers to classify the
object category. NetT has multiple branches, corresponding to multiple
categories, to distinguish the tracked object from the background. Since each
branch in NetT is trained by the videos of a specific category or groups of
similar categories, NetT encodes category-based features for tracking. During
online tracking, NetC and NetT jointly determine the target regions with the
right category and foreground labels for target estimation. To improve the
robustness and precision, NetC and NetT inter-supervise each other and trigger
network adaptation when their outputs are ambiguous for the same image regions
(i.e., when the category label contradicts the foreground/background
classification). We have compared the performance of our tracker to other
state-of-the-art trackers on a large-scale tracking benchmark (100
sequences)---the obtained results demonstrate the effectiveness of our proposed
tracker as it outperformed other 38 state-of-the-art tracking algorithms.
