Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 30, Issue 10, Pages 3727-3739Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2019.2945068
Keywords
Target tracking; Visualization; Correlation; Object tracking; Neural networks; Task analysis; Feature extraction; Visual object tracking; discriminative correlation filter; lasso regression
Categories
Funding
- National Natural Science Foundation of China [61672265, U1836218]
- 111 Project of Ministry of Education of China [B12018]
- EPSRC [EP/N007743/1, EP/R018456/1]
- EPSRC [EP/R018456/1, EP/N007743/1] Funding Source: UKRI
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Discriminative correlation filter (DCF) has achieved advanced performance in visual object tracking with remarkable efficiency guaranteed by its implementation in the frequency domain. However, the effect of the structural relationship of DCF and object features has not been adequately explored in the context of the filter design. To remedy this deficiency, this paper proposes a Low-rank and Sparse DCF (LSDCF) that improves the relevance of features used by discriminative filters. To be more specific, we extend the classical DCF paradigm from ridge regression to lasso regression, and constrain the estimate to be of low-rank across frames, thus identifying and retaining the informative filters distributed on a low-dimensional manifold. To this end, specific temporal-spatial-channel configurations are adaptively learned to achieve enhanced discrimination and interpretability. In addition, we analyse the complementary characteristics between hand-crafted features and deep features, and propose a coarse-to-fine heuristic tracking strategy to further improve the performance of our LSDCF. Last, the augmented Lagrange multiplier optimisation method is used to achieve efficient optimisation. The experimental results obtained on a number of well-known benchmarking datasets, including OTB2013, OTB50, OTB100, TC128, UAV123, VOT2016 and VOT2018, demonstrate the effectiveness and robustness of the proposed method, delivering outstanding performance compared to the state-of-the-art trackers.
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