摘要
在线多目标跟踪是实时视频序列分析的重要前提。针对在线多目标跟踪中目标检测可靠性低、跟踪丢失较多、轨迹不平滑等问题,提出了基于R-FCN网络框架的多候选关联的在线多目标跟踪模型。首先,通过基于R-FCN网络从KF预测结果和检测结果中获取更可靠的候选框,然后利用Siamese网络进行基于外观特征的相似性度量,实现候选与轨迹之间的数据关联,最后通过RANSAC算法优化跟踪轨迹。在人流密集和目标被部分遮挡的复杂场景中,提出的算法具有较高的目标识别和跟踪能力,大幅减少漏检和误检现象,跟踪轨迹更加连续平滑。实验结果表明,在同等条件下,与当前已有的方法对比,本文提出在目标跟踪准确度(MOTA)、丢失轨迹数(ML)和误报次数(FN)等多个性能指标均有较大提升。
Online multi-target tracking is an important prerequisite for real-time video sequence analysis.Because of low reliability in target detection,high tracking loss rate and unsmooth trajectory in online multi-target tracking,an online multi-target tracking model based on R-FCN(region based fully convolutional networks)network framework is proposed.Firstly,the target evaluation function based on R-FCN network framework is used to select more reliable candidates in the next frame between KF and detection results.Second,the Siamese network is used to perform similarity measurement based on appearance features to complete the match between candidates and tracks.Finally,the tracking trajectory is optimized by the RANSAC(random sample consensus)algorithm.In crowded and partially occluded complex scenes,the proposed algorithm has higher target recognition ability,greatly reduces the phenomenon of missed detection and false detection,and the tracking track is more continuous and smooth.The experimental results show that under the same conditions,compared with the existing methods,the performance indicators of the proposed method,such as target tracking accuracy(MOTA),number of lost trajectories(ML)and number of false positives(FN),have been greatly improved.
作者
鄂贵
王永雄
E Gui;Wang Yongxiong(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《光电工程》
CAS
CSCD
北大核心
2020年第1期29-37,共9页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(61673276,61703277)~~
关键词
多目标跟踪
候选模型
孪生网络
轨迹估计
multi-target tracking
candidate model
Siamese network
trajectory estimation
