2010
DOI: 10.1007/s10694-010-0174-9
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Automation of Tracking Trajectories in a Crowded Situation

Abstract: Studies on pedestrians using microscopic simulation require large amounts of trajectory data from real-world pedestrian crowds. The collection of such data, if done manually, involves tremendous efforts and is very time-consuming. Although many studies have asserted the possibility of automating this task using video cameras, we have found that only a few have demonstrated good performance in very crowded situations or from a top-angled view scene. This paper deals with tracking pedestrian crowd under heavy oc… Show more

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Cited by 6 publications
(2 citation statements)
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“…Papers like [13][14][15][16][17] all report a false detection rate of more than 10%. Typically the decrease in the false detection rate induces the increase in false positive detections.…”
Section: Related Workmentioning
confidence: 99%
“…Papers like [13][14][15][16][17] all report a false detection rate of more than 10%. Typically the decrease in the false detection rate induces the increase in false positive detections.…”
Section: Related Workmentioning
confidence: 99%
“…In order to derive possible heading angle changes of workers, various literatures for pedestrian model have been reviewed. Saadat et al 24 under took a detailed survey on previous pedestrian models. Løva˚s 25 presented a stochastic model based on the following assumptions: Any pedestrian facility can be modeled as a network of walkway sections.…”
Section: Predictive Environment Monitoringmentioning
confidence: 99%