Real-Time Visual Object Tracking via Few-Shot Learning

Abstract

Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot Learning (FSL). While the concept of FSL is not new in tracking and has been previously applied by prior works, most of them are tailored to fit specific types of FSL algorithms and may sacrifice running speed. In this work, we propose a generalized two-stage framework that is capable of employing a large variety of FSL algorithms while presenting faster adaptation speed. The first stage uses a Siamese Regional Proposal Network to efficiently propose the potential candidates and the second stage reformulates the task of classifying these candidates to a few-shot classification problem. Following such a coarse-to-fine pipeline, the first stage proposes informative sparse samples for the second stage, where a large variety of FSL algorithms can be conducted more conveniently and efficiently. As substantiation of the second stage, we systematically investigate several forms of optimization-based few-shot learners from previous works with different objective functions, optimization methods, or solution space. Beyond that, our framework also entails a direct application of the majority of other FSL algorithms to visual tracking, enabling mutual communication between researchers on these two topics. Extensive experiments on the major benchmarks, VOT2018, OTB2015, NFS, UAV123, TrackingNet, and GOT-10k are conducted, demonstrating a desirable performance gain and a real-time speed.

References in corpus (11)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• High-Speed Tracking with Kernelized Correlation Filters
• Prototypical Networks for Few-shot Learning
• GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
• On First-Order Meta-Learning Algorithms
• One-shot Learning with Memory-Augmented Neural Networks
• OptNet: Differentiable Optimization as a Layer in Neural Networks
• GradNet: Gradient-Guided Network for Visual Object Tracking
• RPT: Learning Point Set Representation for Siamese Visual Tracking
• Learning Feature Embeddings for Discriminant Model based Tracking
• ROAM: Recurrently Optimizing Tracking Model