Exploiting contextual data for event retrieval in surveillance video
Proceedings of the ACM International Conference on Image and Video Retrieval
Multi-cue-based crowd segmentation in stereo vision
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Robust object tracking in crowd dynamic scenes using explicit stereo depth
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Automated pedestrian detection, counting, and tracking have received significant attention in the computer vision community of late. As such, a variety of techniques have been investigated using both traditional 2-D computer vision techniques and, more recently, 3-D stereo information. However, to date, a quantitative assessment of the performance of stereo-based pedestrian detection has been problematic, mainly due to the lack of standard stereo-based test data and an agreed methodology for carrying out the evaluation. This has forced researchers into making subjective comparisons between competing approaches. In this paper, we propose a framework for the quantitative evaluation of a short-baseline stereo-based pedestrian detection system. We provide freely available synthetic and real-world test data and recommend a set of evaluation metrics. This allows researchers to benchmark systems, not only with respect to other stereo-based approaches, but also with more traditional 2-D approaches. In order to illustrate its usefulness, we demonstrate the application of this framework to evaluate our own recently proposed technique for pedestrian detection and tracking.