IEEE Trans Image Process - Counting people with low-level features and Bayesian regression.

Tópicos

{ model(3404) distribut(989) bayesian(671) }
{ motion(1329) object(1292) video(1091) }
{ estim(2440) model(1874) function(577) }
{ sampl(1606) size(1419) use(1276) }
{ imag(2675) segment(2577) method(1081) }
{ method(1219) similar(1157) match(930) }
{ visual(1396) interact(850) tool(830) }
{ learn(2355) train(1041) set(1003) }
{ featur(1941) imag(1645) propos(1176) }
{ first(2504) two(1366) second(1323) }
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{ problem(2511) optim(1539) algorithm(950) }
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{ studi(1119) effect(1106) posit(819) }
{ state(1844) use(1261) util(961) }
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{ analysi(2126) use(1163) compon(1037) }
{ use(976) code(926) identifi(902) }
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Resumo

An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed. Instead, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic-texture motion model. A set of holistic low-level features is extracted from each segmented region, and a function that maps features into estimates of the number of people per segment is learned with Bayesian regression. Two Bayesian regression models are examined. The first is a combination of Gaussian process regression with a compound kernel, which accounts for both the global and local trends of the count mapping but is limited by the real-valued outputs that do not match the discrete counts. We address this limitation with a second model, which is based on a Bayesian treatment of Poisson regression that introduces a prior distribution on the linear weights of the model. Since exact inference is analytically intractable, a closed-form approximation is derived that is computationally efficient and kernelizable, enabling the representation of nonlinear functions. An approximate marginal likelihood is also derived for kernel hyperparameter learning. The two regression-based crowd counting methods are evaluated on a large pedestrian data set, containing very distinct camera views, pedestrian traffic, and outliers, such as bikes or skateboarders. Experimental results show that regression-based counts are accurate regardless of the crowd size, outperforming the count estimates produced by state-of-the-art pedestrian detectors. Results on 2 h of video demonstrate the efficiency and robustness of the regression-based crowd size estimation over long periods of time.

Resumo Limpo

approach problem estim size inhomogen crowd compos pedestrian travel differ direct without use explicit object segment track propos instead crowd segment compon homogen motion use mixtur dynamictextur motion model set holist lowlevel featur extract segment region function map featur estim number peopl per segment learn bayesian regress two bayesian regress model examin first combin gaussian process regress compound kernel account global local trend count map limit realvalu output match discret count address limit second model base bayesian treatment poisson regress introduc prior distribut linear weight model sinc exact infer analyt intract closedform approxim deriv comput effici kerneliz enabl represent nonlinear function approxim margin likelihood also deriv kernel hyperparamet learn two regressionbas crowd count method evalu larg pedestrian data set contain distinct camera view pedestrian traffic outlier bike skateboard experiment result show regressionbas count accur regardless crowd size outperform count estim produc stateoftheart pedestrian detector result h video demonstr effici robust regressionbas crowd size estim long period time

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