FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for object model adaption, which incorporates temporal information with only a single learnable parameter. We further improve the tracker architecture with a pixel-wise fusion block. By plugging-in sophisticated backbones with the abovementioned modules, FEAR-M and FEAR-L trackers surpass most Siamese trackers on several academic benchmarks in both accuracy and efficiency. 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 with superior accuracy. In addition, we expand the definition of the model efficiency by introducing FEAR benchmark that assesses energy consumption and execution speed. We show that energy consumption is a limiting factor for trackers on mobile devices. Source code, pretrained models, and evaluation protocol are available at https://github.com/PinataFarms/FEARTracker.
Code (1)
Tasks
Visual Object TrackingSimilar Papers 제목 키워드 기반
Tracking-by-Trackers with a Distilled and Reinforced Model
Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we unify such goals by proposing a novel t…
Knowledge DistillationObject TrackingVideo Object TrackingVisual Object Tracking+1Efficient Adversarial Attacks for Visual Object Tracking
Visual object tracking is an important task that requires the tracker to find the objects quickly and accurately. The existing state-ofthe-art object trackers, i.e., Siamese based trackers, use DNNs to attain high accura…
GPUObjectObject TrackingVisual Object Tracking+1Mobile Vision Transformer-based Visual Object Tracking
The introduction of robust backbones, such as Vision Transformers, has improved the performance of object tracking algorithms in recent years. However, these state-of-the-art trackers are computationally expensive since …
GPUObjectObject TrackingVisual Object TrackingMultiple Context Features in Siamese Networks for Visual Object Tracking
Siamese networks have been successfully utilized to learn a robust matching function between pairs of images. Visual object tracking methods based on siamese networks have been gaining popularity recently due to their ro…
Object TrackingVisual Object TrackingSpiderNets: Vision Models Predict Human Fear From Aversive Images
Phobias are common and impairing, and exposure therapy, which involves confronting patients with fear-provoking visual stimuli, is the most effective treatment. Scalable computerized exposure therapy requires automated p…
Transfer Learning