Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Crowd detection with a multiview sampler
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A 3-D marked point process model for multi-view people detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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
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In the field of multi-view people localization, only a few works consider a non-planar ground surface. In this article we introduce a framework for collecting ground truth data in such case, we show characterization of specific errors and introduce a method to automatically merge multiple ground truth data generated by different users to form a more reliable reference ground truth. We use this reference ground truth to evaluate the error rate, the accuracy and the recall of subjects (6 laymen and 3 with domain knowledge). We show that even laymen can work accurately, but even subjects with domain knowledge miss a number of people in a crowded scene. Our findings show that creating ground truth data requires special attention in this field.