2013
DOI: 10.1007/978-3-642-33926-4_25
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Fast and Robust Multi-people Tracking from RGB-D Data for a Mobile Robot

Abstract: This paper proposes a fast and robust multi-people tracking algorithm for mobile platforms equipped with a RGB-D sensor. Our approach features an efficient point cloud depth-based clustering, an HOG-like classification to robustly initialize a person tracking and a person classifier with online learning to manage the person ID matching even after a full occlusion. For people detection, we make the assumption that people move on a ground plane. Tests are presented on a challenging real-world indoor environment … Show more

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Cited by 30 publications
(25 citation statements)
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“…In these scenarios, a reasonable assumption is that at least the subject's upper body is visible: then, the detection problem is solved through simpler techniques such as: looking for obvious person markers (e.g. the head [38]); filtering point clusters matching the expected approximate dimensions of a person (most importantly, the height [9]); or using statistical classifiers trained on the whole shape of the person [32]. However, in order to ensure that the subject's upper body is visible, the viewpoint can not lie too close to the subject; moreover, in order to avoid occlusions, expecially in crowded or cluttered environments, the sensor should stay in an elevated position, at least at the level of the subjects' chest or eyes.…”
Section: Related Workmentioning
confidence: 99%
“…In these scenarios, a reasonable assumption is that at least the subject's upper body is visible: then, the detection problem is solved through simpler techniques such as: looking for obvious person markers (e.g. the head [38]); filtering point clusters matching the expected approximate dimensions of a person (most importantly, the height [9]); or using statistical classifiers trained on the whole shape of the person [32]. However, in order to ensure that the subject's upper body is visible, the viewpoint can not lie too close to the subject; moreover, in order to avoid occlusions, expecially in crowded or cluttered environments, the sensor should stay in an elevated position, at least at the level of the subjects' chest or eyes.…”
Section: Related Workmentioning
confidence: 99%
“…Basso et al. () proposed a multiperson tracking algorithm for mobile platforms equipped with an RGB‐D sensor. Their approach features a point‐cloud depth‐based clustering, an HOG‐like classification to initialize a pedestrian tracker and classifier with online learning to manage the person ID matching.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Although the approach is effective in slight occlusion scene, its performance degrades increasingly in scenarios where visual and depth clutters occur simultaneously. Basso et al (2013) proposed a multiperson tracking algorithm for mobile platforms equipped with an RGB-D sensor. Their approach features a point-cloud depthbased clustering, an HOG-like classification to initialize a pedestrian tracker and classifier with online learning to manage the person ID matching.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Basso et a1. [1] Similar online boosting technique, using three types of RGB-D features and the confidence maximization search in 3D space, was used to build people models for tracking by Lueber et a1. [2].…”
Section: A Identification With Rgb-d Cameramentioning
confidence: 99%