Do tracking by clustering anchors output from region proposal network
Most existing clustering algorithms suffer from the computation of similarity function and the representation of each object.In this paper,we propose a clustering tracker based on region proposal network(RPN-C)to do tracking by clustering anchors output by region proposal network into potential centers.We first cut off the second part of Faster RCNN and then cast clustering algorithms in feature space of anchors,including K-Means,mean shift and density peak clustering strategy in terms of anchors centroid and scale information.Without fully connected layers,the RPN-C tracker can lower the computational cost up to 60%and still,it can effectively maintain an accurate prediction for the localization in next frame.To evaluate the robustness of this tracker,we establish a dataset containing over 2000 training images and 7 testing sequences of 8 kinds of fruits.The experimental results on our own datasets demonstrate that the proposed tracker performs excellently both in location of object and the decision of scale and has a strong advantage of stability in the context of occlusion and complicated background.
Yanke Wang Qidan Zhu Wenchang Nie Xiao Hong
Harbin Engineering University,College of Automation,Institute of Intelligent Control,150001 Harbin,China
国际会议
2018 International Symposium on Water System Operations(ISWRSO 2018)(2018年水资源系统及调度国际研讨会)
北京
英文
1-4
2018-10-12（万方平台首次上网日期，不代表论文的发表时间）