Se Jin Cho, Wonwoo Cho, Dongmin Choi, Gyuhyeon Sim, So Yeong Jeong, Sung Hyun Baik, Yun Jung Bae, Byung Se Choi, Jae Hyoung Kim, Sooyoung Yoo, Jung Ho Han, Chae-Yong Kim, Jaegul Choo, Leonard Sunwoo
Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data
Scientific reports
paper proposes efficient system integrates multiple vision models robust multiperson detection tracking mobile service social robots public environments core technique novel maximum likelihood mlbased algorithm combines multimodel detections meanshift tracking first likelihood probability integrates detections similarity local appearance defined expectationmaximization emlike meanshift algorithm derived ml framework iteration estep estimates associations detections mstep locates new position according ml criterion robust complex crowded scenarios multiperson tracking improved sequential strategy perform meanshift tracking proposed strategy human objects tracked sequentially according priority order balance efficiency robustness realtime performance stage first two objects list priority order tested one higher score selected proposed method successfully implemented realworld service social robots vision system integrates stereobased histogramsoforientedgradientsbased human detections occlusion reasoning sequential meanshift tracking various examples show advantages robustness proposed system multiperson tracking mobile robots presented quantitative evaluations performance multiperson tracking also performed experimental results indicate significant improvements achieved using proposed method