西安电子科技大学学报（自然科学版）2019，Vol.46Issue(1)：117-123,7.DOI:10.19665/j.issn1001-2400.2019.01.019
自适应权值卷积特征的鲁棒目标跟踪算法
Robust object tracking via adaptive weight convolutional features
摘要
Abstract
To solve the tracking failure problem in some videos caused by traditional deep learning tracking algorithms with fixed weight convolutional features,this paper proposes a novel tracking method combing the response map and the entropy function which considers the performance of each layer of convolutional neural networks and automatically adjusts the weight parameters.At the same time,an EdgeBoxes detection scheme is introduced when the maximum value of tracking response is less than a given threshold.A great number of bounding boxes are extracted by a sliding window and are evaluated by the EdgeBoxes detection scheme which generates the original proposal bounding boxes.Finally,the tracking method based on the correlation filter are conducted on the original proposal bounding boxes with the update scheme given.We have tested the proposed algorithm and nine state-of-the-art approaches on OTB-2013video databases.Experimental results demonstrate that the proposed method has a higher precision and overlap rate.关键词
目标跟踪/自适应权值/相关滤波/目标检测Key words
object tracking/adaptive weight/correlation filters/object detection分类
信息技术与安全科学引用本文复制引用
王海军,张圣燕..自适应权值卷积特征的鲁棒目标跟踪算法[J].西安电子科技大学学报（自然科学版）,2019,46(1):117-123,7.基金项目
山东省自然科学基金(ZR2015FL009) （ZR2015FL009）
山东省高等学校科技计划(J17KA088,J16LN02) （J17KA088,J16LN02）
滨州学院科研基金(BZXYL1803) （BZXYL1803）
