  Being able to track an anonymous object, a model-free tracker is
comprehensively applicable regardless of the target type. However, designing
such a generalized framework is challenged by the lack of object-oriented prior
information. As one solution, a real-time model-free object tracking approach
is designed in this work relying on Convolutional Neural Networks (CNNs). To
overcome the object-centric information scarcity, both appearance and motion
features are deeply integrated by the proposed AMNet, which is an end-to-end
offline trained two-stream network. Between the two parallel streams, the ANet
investigates appearance features with a multi-scale Siamese atrous CNN,
enabling the tracking-by-matching strategy. The MNet achieves deep motion
detection to localize anonymous moving objects by processing generic motion
features. The final tracking result at each frame is generated by fusing the
output response maps from both sub-networks. The proposed AMNet reports leading
performance on both OTB and VOT benchmark datasets with favorable real-time
processing speed.
