Abstract
Human detection in crowded situations represents a challenging task in many practically relevant scenarios. In this paper we propose a passive stereo depth based human detection scheme employing a hierarchically-structured tree of learned shape templates for delineating clusters corresponding to humans. In order to enhance the specificity of the depth-based detection approach towards humans, we also incorporate a visual object recognition modality in form of a deeply-trained model. We propose a simple way to combine the depth and appearance modalities to better cope with complex effects such as heavily occluded and small-sized humans, and clutter. Obtained results are analyzed in terms of improvements and shortcomings introduced by the individual detection modalities. Our proposed combination achieves a good accuracy at a decent computational speed in difficult scenarios exhibiting crowded situations. Hence in our view, the presented concepts represent a detection scheme of practical relevance.
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Acknowledgements
The authors thank both the Austrian Federal Ministry for Transport, Innovation and Technology as well as the Austrian Research Promotion Agency (FFG) for co-funding the research project “LEAL” (FFG Nr. 850218) within the National Research Development Programme KIRAS Austria.
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Beleznai, C., Steininger, D., Broneder, E. (2020). Human Detection in Crowded Situations by Combining Stereo Depth and Deeply-Learned Models. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_47
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