IEEE Trans Pattern Anal Mach Intell - Contextualized Trajectory Parsing with Spatio-Temporal Graph.

Tópicos

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{ structur(1116) can(940) graph(676) }
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{ imag(2830) propos(1344) filter(1198) }
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{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ extract(1171) text(1153) clinic(932) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This work investigates how to automatically parse object trajectories in surveillance videos, that aims to jointly solve three subproblems: i) spatial segmentation, ii) temporal tracking, and iii) object categorization. We present a novel representation spatio-temporal graph (ST-Graph), in which: i) graph nodes express the motion primitives, each representing a short sequence of small-size patches over consecutive images; and ii) every two neighbor nodes are linked with either a positive edge or a negative edge to describe their collaborative or exclusive relationship of belonging to the same object trajectory. Phrasing the trajectory parsing as a graph multi-coloring problem, we propose a unified probabilistic formulation to integrate various types of context knowledge as informative priors. An efficient composite cluster sampling algorithm is employed in search of the optimal solution by exploiting both the collaborative and the exclusive relationships between nodes. The proposed framework is evaluated over challenging videos from public datasets, and results show that it can achieve state-of-the-art tracking accuracy.

Resumo Limpo

work investig automat pars object trajectori surveil video aim joint solv three subproblem spatial segment ii tempor track iii object categor present novel represent spatiotempor graph stgraph graph node express motion primit repres short sequenc smallsiz patch consecut imag ii everi two neighbor node link either posit edg negat edg describ collabor exclus relationship belong object trajectori phrase trajectori pars graph multicolor problem propos unifi probabilist formul integr various type context knowledg inform prior effici composit cluster sampl algorithm employ search optim solut exploit collabor exclus relationship node propos framework evalu challeng video public dataset result show can achiev stateoftheart track accuraci

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