IEEE Trans Pattern Anal Mach Intell - Discriminative Video Pattern Search for Efficient Action Detection.

Tópicos

{ state(1844) use(1261) util(961) }
{ motion(1329) object(1292) video(1091) }
{ detect(2391) sensit(1101) algorithm(908) }
{ method(1219) similar(1157) match(930) }
{ problem(2511) optim(1539) algorithm(950) }
{ data(1737) use(1416) pattern(1282) }
{ algorithm(1844) comput(1787) effici(935) }
{ learn(2355) train(1041) set(1003) }
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{ care(1570) inform(1187) nurs(1089) }
{ featur(1941) imag(1645) propos(1176) }
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{ assess(1506) score(1403) qualiti(1306) }
{ chang(1828) time(1643) increas(1301) }
{ method(984) reconstruct(947) comput(926) }
{ howev(809) still(633) remain(590) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
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{ group(2977) signific(1463) compar(1072) }
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{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
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{ survey(1388) particip(1329) question(1065) }
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{ activ(1452) weight(1219) physic(1104) }
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Resumo

Actions are spatiotemporal patterns. Similar to the sliding window-based object detection, action detection finds the reoccurrences of such spatiotemporal patterns through pattern matching, by handling cluttered and dynamic backgrounds and other types of action variations. We address two critical issues in pattern matching-based action detection: 1) the intrapattern variations in actions, and 2) the computational efficiency in performing action pattern search in cluttered scenes. First, we propose a discriminative pattern matching criterion for action classification, called naive Bayes mutual information maximization (NBMIM). Each action is characterized by a collection of spatiotemporal invariant features and we match it with an action class by measuring the mutual information between them. Based on this matching criterion, action detection is to localize a subvolume in the volumetric video space that has the maximum mutual information toward a specific action class. A novel spatiotemporal branch-and-bound (STBB) search algorithm is designed to efficiently find the optimal solution. Our proposed action detection method does not rely on the results of human detection, tracking, or background subtraction. It can handle action variations such as performing speed and style variations as well as scale changes well. It is also insensitive to dynamic and cluttered backgrounds and even to partial occlusions. The cross-data set experiments on action detection, including KTH, CMU action data sets, and another new MSR action data set, demonstrate the effectiveness and efficiency of the proposed multiclass multiple-instance action detection method.

Resumo Limpo

action spatiotempor pattern similar slide windowbas object detect action detect find reoccurr spatiotempor pattern pattern match handl clutter dynam background type action variat address two critic issu pattern matchingbas action detect intrapattern variat action comput effici perform action pattern search clutter scene first propos discrimin pattern match criterion action classif call naiv bay mutual inform maxim nbmim action character collect spatiotempor invari featur match action class measur mutual inform base match criterion action detect local subvolum volumetr video space maximum mutual inform toward specif action class novel spatiotempor branchandbound stbb search algorithm design effici find optim solut propos action detect method reli result human detect track background subtract can handl action variat perform speed style variat well scale chang well also insensit dynam clutter background even partial occlus crossdata set experi action detect includ kth cmu action data set anoth new msr action data set demonstr effect effici propos multiclass multipleinst action detect method

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