Particle Filter for Trajectories of Movers from Laser Scanned Dataset

Sharif, Md. Haidar

doi:10.1007/978-3-030-37548-5_11

Cited by 4 publications

(15 citation statements)

References 61 publications

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“…From Fig. 12, it is noticeable that the algorithm of Sharif [8] became the best performing one, with the longest bars of 0.6428, 0.0783, and 0.5844 for Friedman test [184], Friedman's aligned rank test [186], and Quade test [187], respectively. This hints that algorithm of Sharif [8] gives great performance for the solution of underlaying problems of detecting and tracking both pedestrians and vehicles from laser scanned data points.…”

Section: B Average Ranking Of Algorithmsmentioning

confidence: 98%

“…As a sample, Fig. 4 views the graphical abstract of the algorithm of Sharif [8]; where (a) points to the laser scanner of LMS-511 and a real world video frame; (b) depicts the obtained blobs (as colored in blue) for all laser scanned data points per frame; (c) denotes the foreground data points as colored in red; (d) hints the extracted movers as marked by white points and the L-shaped structure belongs to a vehicle, while others are most likely pedestrians; (e) displays recognized record of SVM; and (f) shows trajectories of movers for several frames.…”

Section: Prominent People-vehicles Related Algorithms a Selected Algorithms And Flow Diagramsmentioning

confidence: 99%

“…A sufficiently large number of particles can handle any kind of probability distribution. Inspired by these facts, Sharif [8] proposed SVM along with Hungarian method and particle filter to get trajectories of movers. On the same dataset (e.g., Galip et al [7]), the algorithm of Sharif [8] reported the best minimization of error rates.…”

Section: B Qualitative Description Of Algorithmsmentioning

confidence: 99%

“…In the case of 1 × N comparisons, the post-hoc procedures consist of Bonferroni-Dunn's [188], Holm's [189], Hochberg's [190], Hommel's [191], [192], Holland's [193], Rom's [194], Finner's [195], and Li's [196] procedures. In these statistical analysis tests, multiple comparison post-hoc procedures considered for comparing the control algorithm of Sharif [8] with the others. The results are shown by computing p-values for each comparison.…”

Section: Post-hoc Procedures For 1 × N Comparisonsmentioning

confidence: 99%

“…Nowadays, like home automation [3]- [5], traffic au-tomation became one of the key factors for a smart city [6]. The sidewalk occupancy is a serious problem in the urban life [7], [8]. If sidewalk occupancy will occur, then pedestrians will tend to walk on the street which would lead many potential traffic hazards.…”

Section: Introductionmentioning

confidence: 99%

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Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Sharif

2021

IEEE Access

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Privacy of people is a key factor in surveillance systems. Video camera brings us welloff color information. How would the privacy be secured then? Besides, privacy protection should not create a hindrance for finding of objects or people under specific cases. Laser scanner takes way affluent color information. It functions with eye-safe and invisible laser beam. Yet, it provides us robust object recognition map. Images can be interpreted by humans, but laser-based systems need software applications to explain the data. Camera-based surveillance system does not focus on the problem of private life conservation. On the contrary, laser-based surveillance system ensures privacy of people inherently, as it does not record real world videos except laser scanned data points. In this paper, first, the privacy issues of people for both surveillance systems have been compared to realize their significance. Second, a qualitative performance comparison between laser-based and RGB camera-based systems has been made to hint that laser-based algorithms should be used instead of common RGB cameras. Third, a succinct survey of laser-based detection and tracking algorithms of movers has been conducted. Final, a superiority measure of the leading laser-based people-vehicles related algorithms has been performed on the basis of statistical test scores deeming the ineffectualness metrics (e.g., errors and failures) of each algorithm.

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Section: B Average Ranking Of Algorithmsmentioning

confidence: 98%

Section: Prominent People-vehicles Related Algorithms a Selected Algorithms And Flow Diagramsmentioning

confidence: 99%

Section: B Qualitative Description Of Algorithmsmentioning

confidence: 99%

Section: Post-hoc Procedures For 1 × N Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Sharif

2021

IEEE Access

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Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

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Leg tracking is an established field in mobile robotics and machine vision in general.These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail.Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state-of-the-art performance, and that laser and fusion tracker outperform recent methods.

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