FEAR: Fast, Efficient, Accurate and Robust Visual Tracker

• URL: http://arxiv.org/abs/2112.07957v1
• Date: Wed, 15 Dec 2021 08:28:55 GMT
• Title: FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
• Authors: Vasyl Borsuk, Roman Vei, Orest Kupyn, Tetiana Martyniuk, Igor Krashenyi, Ji\v{r}i Matas
• Abstract summary: We present FEAR, a novel, fast, efficient, accurate, and robust Siamese visual tracker. FEAR-XS tracker is 2.4x smaller and 4.3x faster than LightTrack [62] with superior accuracy.
• Score: 2.544539499281093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present FEAR, a novel, fast, efficient, accurate, and robust Siamese visual tracker. We introduce an architecture block for object model adaption, called dual-template representation, and a pixel-wise fusion block to achieve extra flexibility and efficiency of the model. The dual-template module incorporates temporal information with only a single learnable parameter, while the pixel-wise fusion block encodes more discriminative features with fewer parameters compared to standard correlation modules. By plugging-in sophisticated backbones with the novel modules, FEAR-M and FEAR-L trackers surpass most Siamesetrackers on several academic benchmarks in both accuracy and efficiencies. Employed with the lightweight backbone, the optimized version FEAR-XS offers more than 10 times faster tracking than current Siamese trackers while maintaining near state-of-the-art results. FEAR-XS tracker is 2.4x smaller and 4.3x faster than LightTrack [62] with superior accuracy. In addition, we expand the definition of the model efficiency by introducing a benchmark on energy consumption and execution speed. Source code, pre-trained models, and evaluation protocol will be made available upon request

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