Learning Multi-Task Target-Specific Correlation Filters for Robust Tracking

In recent years, correlation filter based trackers have shown great potentials in visual tracking because of their high computational efficiency and low memory consumption. However, their increasing tracking performance typically comes at the cost of sacrificing the computational speed and memory usage. Furthermore, training high-dimensional correlation filters with a large number of parameters usually introduces the risk of over-fitting. In this paper, we propose Multi-Task Target-Specific Correlation Filters (MTSCF) to tackle the above issues. First, we construct a novel regression formulation for multi-task filter learning to promote both competition and collaboration among correlation filters to select discriminative features for robust tracking. This significantly reduces redundancies among features at both spatial level and channel level, which produces sparse correlation filters. Then, we develop an effective filter importance evaluation criterion according to the expansion of designed regression formulation to choose a set of target-specific features for efficient tracking. This significantly reduces the number of filter parameters, which further results in compact correlation filters. Moreover, we propose to efficiently optimize the proposed MTSCF via an Alternating Direction Method of Multipliers (ADMM) algorithm. Evaluation results on six challenging benchmark datasets (i.e., OTB2013, OTB2015, VOT2016, VOT2018, UAV20L and LaSOT) show the proposed method performs favorably against existing state-of-the-art DCF based trackers, and it retains a high speed of 40 FPS on a CPU when evaluated with only hand-crafted features.