8,095 research outputs found
Robust pedestrian detection and tracking in crowded scenes
In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases
Likelihood inference for particle location in fluorescence microscopy
We introduce a procedure to automatically count and locate the fluorescent
particles in a microscopy image. Our procedure employs an approximate
likelihood estimator derived from a Poisson random field model for photon
emission. Estimates of standard errors are generated for each image along with
the parameter estimates, and the number of particles in the image is determined
using an information criterion and likelihood ratio tests. Realistic
simulations show that our procedure is robust and that it leads to accurate
estimates, both of parameters and of standard errors. This approach improves on
previous ad hoc least squares procedures by giving a more explicit stochastic
model for certain fluorescence images and by employing a consistent framework
for analysis.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS299 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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