西安电子科技大学学报（自然科学版）2019，Vol.46Issue(1)：57-63,7.DOI:10.19665/j.issn1001-2400.2019.01.010
融合ELM和相关滤波的鲁棒性目标跟踪算法
Robust target tracking algorithm based on the ELM and discriminative correlation filter
摘要
Abstract
In order to solve the problem that the tracking results fall into the local minimum easily and the feature extraction process is too slow due to the utilization of deep learning,we study the robust object tracking algorithm based on the Extreme Learning Machine(ELM)and Discriminative Correlation Filter (DCF).Based on the C-COT algorithm,our method improves its feature extraction way and the optimization method for the confidence map.First,a new feature extraction model is designed by using the multi-layer ELM sparse autoencoders to extract the image features efficiently and replacing the original Convolutional Neural Network(CNN).Second,after the feature extraction model,an Online Sequential Extreme Learning Machine(OS-ELM)is used to construct the target rough location estimation model and the multi-peak detection method is used to get the predicted rough location of the target.Third,the search area of the confidence map is determined according to the preliminary target location to avoid the tracking result getting into the local minimum.Finally,the effectiveness of the proposed algorithm is tested on three visual tracking benchmarks.Experimental results show that the proposed algorithm is robust to occlusion, motion blur and similar targets and has a tracking speed of 12.9times that of the C-COT,effectively improving the tracking accuracy and speed.关键词
视觉目标跟踪/相关滤波/极限学习机/特征提取/C-COT算法Key words
object tracking/discriminative correlation filter/extreme learning machine/feature extraction/C-COT algorithm分类
信息技术与安全科学引用本文复制引用
王欣远,肖嵩,李磊,焦玲玲..融合ELM和相关滤波的鲁棒性目标跟踪算法[J].西安电子科技大学学报（自然科学版）,2019,46(1):57-63,7.基金项目
国家自然科学基金(61372069) （61372069）
高等学校学科创新引智计划(111计划)(B08038) （111计划）
