70 research outputs found
Efficient Evaluation of the Number of False Alarm Criterion
This paper proposes a method for computing efficiently the significance of a
parametric pattern inside a binary image. On the one hand, a-contrario
strategies avoid the user involvement for tuning detection thresholds, and
allow one to account fairly for different pattern sizes. On the other hand,
a-contrario criteria become intractable when the pattern complexity in terms of
parametrization increases. In this work, we introduce a strategy which relies
on the use of a cumulative space of reduced dimensionality, derived from the
coupling of a classic (Hough) cumulative space with an integral histogram
trick. This space allows us to store partial computations which are required by
the a-contrario criterion, and to evaluate the significance with a lower
computational cost than by following a straightforward approach. The method is
illustrated on synthetic examples on patterns with various parametrizations up
to five dimensions. In order to demonstrate how to apply this generic concept
in a real scenario, we consider a difficult crack detection task in still
images, which has been addressed in the literature with various local and
global detection strategies. We model cracks as bounded segments, detected by
the proposed a-contrario criterion, which allow us to introduce additional
spatial constraints based on their relative alignment. On this application, the
proposed strategy yields state-of the-art results, and underlines its potential
for handling complex pattern detection tasks
Evaluating Crowd Density Estimators via Their Uncertainty Bounds
In this work, we use the Belief Function Theory which extends the
probabilistic framework in order to provide uncertainty bounds to different
categories of crowd density estimators. Our method allows us to compare the
multi-scale performance of the estimators, and also to characterize their
reliability for crowd monitoring applications requiring varying degrees of
prudence
Geometry-Based Multiple Camera Head Detection in Dense Crowds
This paper addresses the problem of head detection in crowded environments.
Our detection is based entirely on the geometric consistency across cameras
with overlapping fields of view, and no additional learning process is
required. We propose a fully unsupervised method for inferring scene and camera
geometry, in contrast to existing algorithms which require specific calibration
procedures. Moreover, we avoid relying on the presence of body parts other than
heads or on background subtraction, which have limited effectiveness under
heavy clutter. We cast the head detection problem as a stereo MRF-based
optimization of a dense pedestrian height map, and we introduce a constraint
which aligns the height gradient according to the vertical vanishing point
direction. We validate the method in an outdoor setting with varying pedestrian
density levels. With only three views, our approach is able to detect
simultaneously tens of heavily occluded pedestrians across a large, homogeneous
area.Comment: Proceedings of the 28th British Machine Vision Conference (BMVC) -
5th Activity Monitoring by Multiple Distributed Sensing Workshop, 201
Determining Epipole Location Integrity by Multimodal Sampling
International audienceIn urban cluttered scenes, a photo provided by a wear-able camera may be used by a walking law-enforcement agent as an additional source of information for localizing themselves, or elements of interest related to public safety and security. In this work, we study the problem of locating the epipole, corresponding to the position of the moving camera, in the field of view of a reference camera. We show that the presence of outliers in the standard pipeline for camera relative pose estimation not only prevents the correct estimation of the epipole localization but also degrades the standard uncertainty propagation for the epipole position. We propose a robust method for constructing an epipole location map, and we evaluate its accuracy as well as its level of integrity with respect to standard approaches
Wide baseline pose estimation from video with a density-based uncertainty model
International audienceRobust wide baseline pose estimation is an essential step in the deployment of smart camera networks. In this work, we highlight some current limitations of conventional strategies for relative pose estimation in difficult urban scenes. Then, we propose a solution which relies on an adaptive search of corresponding interest points in synchronized video streams which allows us to converge robustly toward a high-quality solution. The core idea of our algorithm is to build across the image space a nonstationary mapping of the local pose estimation uncertainty, based on the spatial distribution of interest points. Subsequently, the mapping guides the selection of new observations from the video stream in order to prioritize the coverage of areas of high uncertainty. With an additional step in the initial stage, the proposed algorithm may also be used for refining an existing pose estimation based on the video data; this mode allows for performing a data-driven self-calibration task for stereo rigs for which accuracy is critical, such as onboard medical or vehicular systems. We validate our method on three different datasets which cover typical scenarios in pose estimation. The results show a fast and robust convergence of the solution, with a significant improvement, compared to single image-based alternatives, of the RMSE of ground-truth matches, and of the maximum absolute error
Combination of partially non-distinct beliefs: The cautious-adaptive rule
International audienc
Combination of partially non-distinct beliefs: The cautious-adaptive rule
International audienc
Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework
International audienceWe are interested in the performance of currently available algorithms for the detection of cracks in the specific context of aerial inspection, which is characterized by image quality degradation. We focus on two widely used families of algorithms based on minimal cost path analysis and on image percolation, and we highlight their limitations in this context. Furthermore, we propose an improved strategy based on a-contrario modeling which is able to withstand significant motion blur due to the absence of various thresholds which are usually required in order to cope with varying crack appearances and with varying levels of degradation. The experiments are performed on real image datasets to which we applied complex blur, and the results show that the proposed strategy is effective, while other methods which perform well on good quality data experience significant difficulties with degraded images
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