东南大学学报（自然科学版）2017，Vol.47Issue(1)：1-5,5.DOI:10.3969/j.issn.1001-0505.2017.01.001
基于深度特征的目标跟踪算法
Object tracking algorithm based on deep feature
摘要
Abstract
To solve the robustness problem of the motion object in the tracking process,a tracking al-gorithm based on deep feature is proposed.First,each frame in the video is normalized by affine transformation.Then,the object feature is extracted from the normalized image by the stacked de-noising autoencoder.Because of the large dimensions of deep feature,to improve the computational efficiency,an effective dimension reduction method based on sparse representation is presented.The high dimensional features are projected into the low dimensional space by the projection matrix.The object tracking is achieved by combing the particle filter algorithm.Finally,the object information of the first frame is integrated into the updating process of the object appearance to reduce the risk of object drift during the tracking process.The experimental results show that the proposed tracking al-gorithm exhibits a high degree of accuracy in six video sequences,and it can stably track the object under the circumstance of occlusion,illumination change,scale variation and fast motion.关键词
视觉跟踪/深度学习/稀疏表示/模板更新Key words
visual tracking/deep learning/sparse representation/template updating分类
信息技术与安全科学引用本文复制引用
程旭,张毅锋,刘袁,崔锦实,周琳..基于深度特征的目标跟踪算法[J].东南大学学报（自然科学版）,2017,47(1):1-5,5.基金项目
国家自然科学基金资助项目（61571106）、江苏省自然科学基金资助项目（BK20151102）、北京大学机器感知与智能教育部重点实验室开放课题资助项目（K-2016-03）． （）
