判别式
计算机科学
推论
人工智能
眼动
机器学习
主动外观模型
跟踪(教育)
过程(计算)
特征(语言学)
钥匙(锁)
编码(集合论)
在线模型
任务(项目管理)
计算机视觉
模式识别(心理学)
图像(数学)
工程类
哲学
心理学
集合(抽象数据类型)
程序设计语言
系统工程
操作系统
统计
语言学
计算机安全
数学
教育学
作者
Goutam Bhat,Martin Danelljan,Luc Van Gool,Radu Timofte
出处
期刊:International Conference on Computer Vision
日期:2019-10-01
被引量:484
标识
DOI:10.1109/iccv.2019.00628
摘要
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking. In contrast to most other vision problems, tracking requires the learning of a robust target-specific appearance model online, during the inference stage. To be end-to-end trainable, the online learning of the target model thus needs to be embedded in the tracking architecture itself. Due to the imposed challenges, the popular Siamese paradigm simply predicts a target feature template, while ignoring the background appearance information during inference. Consequently, the predicted model possesses limited target-background discriminability. We develop an end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction. Our architecture is derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations. Furthermore, our approach is able to learn key aspects of the discriminative loss itself. The proposed tracker sets a new state-of-the-art on 6 tracking benchmarks, achieving an EAO score of 0.440 on VOT2018, while running at over 40 FPS. The code and models are available at https://github.com/visionml/pytracking.
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