A multi-view annotation tool for people detection evaluation
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Efficient monte carlo sampler for detecting parametric objects in large scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Evaluation of manually created ground truth for multi-view people localization
Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications
Localizing people in multi-view environment using height map reconstruction in real-time
Pattern Recognition Letters
Detecting parametric objects in large scenes by Monte Carlo sampling
International Journal of Computer Vision
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In this paper we introduce a probabilistic approach on multiple person localization using multiple calibrated camera views. People present in the scene are approximated by a population of cylinder objects in the 3-D world coordinate system, which is a realization of a Marked Point Process. The observation model is based on the projection of the pixels of the obtained motion masks in the different camera images to the ground plane and to other parallel planes with different height. The proposed pixel-level feature is based on physical properties of the 2-D image formation process and can accurately localize the leg position on the ground plane and estimate the height of the people, even if the area of interest is only a part of the scene, meanwhile silhouettes from irrelevant outside motions may significantly overlap with the monitored region in some of the camera views. We introduce an energy function, which contains a data term calculated from the extracted features and a geometrical constraint term modeling the distance between two people. The final configuration results (location and height) are obtained by an iterative stochastic energy optimization process, called the Multiple Birth and Death dynamics. The proposed approached is compared to a recent state-of-the-art technique in a publicly available dataset and its advantages are quantitatively demonstrated.