IEEE Trans Image Process - A novel video dataset for change detection benchmarking.

Tópicos

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{ method(1969) cluster(1462) data(1082) }
{ data(3008) multipl(1320) sourc(1022) }
{ chang(1828) time(1643) increas(1301) }
{ algorithm(1844) comput(1787) effici(935) }
{ perform(999) metric(946) measur(919) }
{ imag(1947) propos(1133) code(1026) }
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{ implement(1333) system(1263) develop(1122) }
{ can(774) often(719) complex(702) }
{ control(1307) perform(991) simul(935) }
{ featur(1941) imag(1645) propos(1176) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ featur(3375) classif(2383) classifi(1994) }
{ framework(1458) process(801) describ(734) }
{ extract(1171) text(1153) clinic(932) }
{ howev(809) still(633) remain(590) }
{ spatial(1525) area(1432) region(1030) }
{ analysi(2126) use(1163) compon(1037) }
{ process(1125) use(805) approach(778) }
{ inform(2794) health(2639) internet(1427) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
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{ patient(2837) hospit(1953) medic(668) }
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{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ drug(1928) target(777) effect(648) }
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Resumo

Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90 000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website and will be updated with feedback from academia and industry in the future.

Resumo Limpo

chang detect one common encount lowlevel task comput vision video process plethora algorithm develop date yet wide accept realist largescal video data set exist benchmark differ method present uniqu chang detect video data set consist near frame video sequenc repres six categori select cover wide rang challeng two modal color thermal infrar distinguish characterist benchmark video data set frame meticul annot hand groundtruth foreground background shadow area boundariesan effort goe much beyond simpl binari label denot presenc chang enabl object precis quantit comparison rank videobas chang detect algorithm paper discuss various aspect new data set quantit perform metric use compar result two dozen chang detect algorithm draw import conclus solv remain issu chang detect describ futur challeng scientif communiti data set evalu tool algorithm rank avail public websit will updat feedback academia industri futur

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