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Learning Disentangled Representation in Pruning for Real-Time UAV Tracking
Proceedings of The 14th Asian Conference on Machine
Learning, PMLR 189:690-705, 2023.
Abstract
Efficiency is a critical issue in UAV tracking
because of the limitations of computing resources,
battery capacity, and maximum load of unmanned
aerial vehicle (UAV). However, deep learning
(DL)-based trackers hardly achieve real-time
tracking on a single CPU despite their high tracking
precision. To the contrary, discriminative
correlation filters (DCF)-based trackers have high
efficiency but their precision is barely
satisfactory. Despite the precision is inferior,
DCF-based trackers instead of DL-based ones are
widely applied in UAV tracking to trade precision
for efficiency. This paper aims to improve the
efficiency of the DL-based tracker SiamFC++, in
particular, for UAV tracking using the model
compression technique, i.e., rank-based filter
pruning, which has not been well explored
before. Meanwhile, to combat the potential loss of
precision caused by pruning we exploit disentangled
representation learning to disentangle the output
feature of the backbone into two parts: the
identity-related features and the identity-unrelated
features. Only the identity-related features are
used for subsequent classification and regression
tasks to improve the effectiveness of the feature
representation. With the proposed disentangled
representation in pruning, we achieved higher
precisions when compressing the original model
SiamFC++ with a global pruning ratio of
0.5. Extensive experiments on four public UAV
benchmarks, i.e., UAV123@10fps, UAVDT, DTB70, and
Vistrone2018, show that the proposed tracker
DP-SiamFC++ strikes a remarkable balance between
efficiency and precision, and achieves
state-of-the-art performance in UAV tracking.