Real-Time Visual Object Tracking via Few-Shot Learning

03/18/2021
by   Jinghao Zhou, et al.
0

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.

READ FULL TEXT

page 4

page 7

07/05/2022

SiamMask: A Framework for Fast Online Object Tracking and Segmentation

In this paper we introduce SiamMask, a framework to perform both visual ...
05/27/2020

AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking

Siamese approaches have achieved promising performance in visual object ...
06/16/2016

Learning feed-forward one-shot learners

One-shot learning is usually tackled by using generative models or discr...
10/23/2020

Rethinking the competition between detection and ReID in Multi-Object Tracking

Due to balanced accuracy and speed, joint learning detection and ReID-ba...
11/08/2020

Faster object tracking pipeline for real time tracking

Multi-object tracking (MOT) is a challenging practical problem for visio...
05/19/2017

Quadruplet Network with One-Shot Learning for Visual Tracking

As a discriminative method of one-shot learning, Siamese deep network al...