最小边界框
计算机科学
子网
人工智能
BitTorrent跟踪器
像素
特征提取
编码(集合论)
模式识别(心理学)
跳跃式监视
回归
卷积神经网络
眼动
图像(数学)
数学
统计
程序设计语言
集合(抽象数据类型)
计算机安全
作者
Dongyan Guo,Jun Wang,Ying Cui,Zhenhua Wang,Shengyong Chen
出处
期刊:Computer Vision and Pattern Recognition
日期:2020-06-14
被引量:239
标识
DOI:10.1109/cvpr42600.2020.00630
摘要
By decomposing the visual tracking task into two subproblems as classification for pixel category and regression for object bounding box at this pixel, we propose a novel fully convolutional Siamese network to solve visual tracking end-to-end in a per-pixel manner. The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction. Different from state-of-the-art trackers like Siamese-RPN, SiamRPN++ and SPM, which are based on region proposal, the proposed framework is both proposal and anchor free. Consequently, we are able to avoid the tricky hyper-parameter tuning of anchors and reduce human intervention. The proposed framework is simple, neat and effective. Extensive experiments and comparisons with state-of-the-art trackers are conducted on challenging benchmarks including GOT-10K, LaSOT, UAV123 and OTB-50. Without bells and whistles, our SiamCAR achieves the leading performance with a considerable real-time speed. The code is available at https://github.com/ohhhyeahhh/SiamCAR.
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