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Alternative TitleReal-Time and Anti-Occlusion Visual Tracking Algorithm Based on Multi-Layer Deep Convolutional Features
崔洲涓; 安军社; 崔天舒
Source Publication光学学报
Keyword机器视觉 目标跟踪 深度卷积特征 相关滤波 模型恢复
Abstract为提高复杂场景中目标跟踪算法的准确性与实时性,提出一种基于多层深度卷积特征的抗遮挡实时目标跟踪算法。针对目标跟踪任务,先对深度卷积网络VGG-Net-19进行微调,再提取目标区域的多层深度卷积特征。根据相关滤波框架构建位置相关滤波器,确定目标中心位置。设计尺度相关滤波器对目标区域进行不同尺度采样,确定目标尺度。目标遮挡时,采用阶段性评估策略进行模型更新与恢复,解决模型误差积累问题。选取目标跟踪评估数据集OTB-2015(100组视频序列)与UAV123(123组视频序列)进行测试。实验结果表明,本文算法具有更高的准确性,能够适应目标遮挡、外观变化及背景干扰等复杂情况,平均速度为29.6 frame/s,满足目标跟踪任务的实时性要求。
Other AbstractIn order to improve the accuracy and real-time performance of visual tracking in complex scenes, a realtime and anti-occlusion visual tracking algorithm based on multi-layer deep convolutional features is proposed. For the visual tracking task, the deep convolutional networks VGG-Net-19 are fine-tuned, and then the multi-layer deep convolutional features of the target region are extracted from the adjusted model. The location correlation filters are constructed to determine the target center position. In order to determine the target scale, a scale correlation filter is performed to sample multi-scale images surrounding the target region. When the target is occluded, the stage evaluation strategy is used to update and recover the model, which solves the problem of template error accumulation. The experimental results on the tracking benchmark OTB-2015 which concludes 100 video sequences and UAV123 which concludes 123 video sequences show that the proposed algorithm has higher accuracy and can adapt to complex situations such as target occlusion, appearance change and background clutters. The average speed is 29.6 frame/s, which meets the real-time requirements of the visual tracking task.
Indexed ByEI ; CSCD
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Document Type期刊论文
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GB/T 7714
崔洲涓,安军社,崔天舒. 基于多层深度卷积特征的抗遮挡实时跟踪算法[J]. 光学学报,2019,39(7).
APA 崔洲涓,安军社,&崔天舒.(2019).基于多层深度卷积特征的抗遮挡实时跟踪算法.光学学报,39(7).
MLA 崔洲涓,et al."基于多层深度卷积特征的抗遮挡实时跟踪算法".光学学报 39.7(2019).
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