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基于孪生卷积神经网络的目标跟踪方法介绍

less than 1 minute read

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前言

Fully-Convolutional Siamese Networks for Object Tracking(Siamese-fc)这篇文章发表在ECCV 2016年的Workshop上, 但是文章在目标跟踪领域却产生了不小的影响。 Siamese-fc在当前大多数基于CNN的目标跟踪方法普遍较慢的情况下,实现了超实时的帧率,在处理3种尺度变换的情况下可以达到86 FPS。 跟踪的效果虽然比不上其它的state-of-the-art的方法,但也取得了较有潜力的结果,在OTB-2013上的AUC达到0.612。 Read more

视觉目标跟踪简介

less than 1 minute read

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前言

视觉目标跟踪是计算机视觉中的一个重要研究方向,有着广泛的应用,如视频监控,人机交互, 无人驾驶等。过去二三十年视觉目标跟踪技术取得了长足的进步,特别是最近两年利用深度学习的目标跟踪方法取得了令人满意的效果,使目标跟踪技术获得了突破性的进展。本文旨在简要介绍目标跟踪的基本流程与框架,目标跟踪存在的挑战,目标跟踪相关方法,以及目标跟踪最新的进展等,希望通过这篇文章能让读者对视觉目标跟踪领域有一个较为全面的认识。 Read more

portfolio

publications

Multi-Task Driven Feature Models for Thermal Infrared Tracking

Published in Proceedings of the 34th AAAI Conference on Artifical Intelligence (AAAI2020), 2020

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR object tracking. Read more

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Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking

Published in IEEE Transactions on Multimedia, 2020

Existing deep Thermal InfraRed (TIR) trackers only use semantic features to represent the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images. To address this issue, we propose a multi-level similarity model under a Siamese framework for robust TIR object tracking. Read more

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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post. Read more

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post. Read more