Learning Disentangled Representation in Pruning for Real-Time UAV Tracking

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.