2019
DOI: 10.1007/978-3-030-04946-1_47
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Human Detection in Crowded Situations by Combining Stereo Depth and Deeply-Learned Models

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Cited by 1 publication
(2 citation statements)
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“…Approaches with increasing sophistication [25], [17] later employed the popular occupancy map concept or the voxel space [24] to delineate individual human candidates. The idea of combining the representational strength of learning on combined RGB-D inputs has been proposed by several papers [41], [26], [4]. Nevertheless, accomplished improvements are rather small.…”
Section: Related State Of the Artmentioning
confidence: 99%
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“…Approaches with increasing sophistication [25], [17] later employed the popular occupancy map concept or the voxel space [24] to delineate individual human candidates. The idea of combining the representational strength of learning on combined RGB-D inputs has been proposed by several papers [41], [26], [4]. Nevertheless, accomplished improvements are rather small.…”
Section: Related State Of the Artmentioning
confidence: 99%
“…To detect human candidates in the depth data, we employ an occupancy map clustering scheme. In the occupancy map, clusters corresponding to humans and compact objects are delineated using a hierarchically-structured tree of learned shape templates [4]. Thus, local grouping within the two-dimensional occupancy map generates consistent object hypotheses and suppresses background clutter and noise.…”
Section: D Multi-object Detection and Trackingmentioning
confidence: 99%