Simultaneous Clustering Classification and Tracking on Point Clouds using Bayesian filter

Sukai Wang; Huaiyang Huang; Ming Liu

doi:10.1109/ROBIO.2017.8324799

Simultaneous Clustering Classification and Tracking on Point Clouds using Bayesian filter

Sukai Wang^*
, Huaiyang Huang
, Ming Liu

^*Corresponding author for this work

CityUHK Shenzhen Research Institute

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

7 Citations (Scopus)

Abstract

Simultaneous Clustering, Classification and Tracking (SCCT) maintains many challenges, especially for point cloud data. SCCT is an essential process to facilitate the autonomous mobile systems. We present a novel unified framework from the object extraction to tracking with real-time performance. The framework can be described as five sub-tasks: ground extraction, clustering, recognition, tracking and representation. We compare the adjacent two frames to solve dense tracking and motion estimation. The state of each clustered object (moving or static) is estimated by using Spatial-Temporal methods. The distinguish objects with different features are extracted. Conditional Random Field and Bayesian filter are adopted to solve the data association problem. All the algorithmic modules have been tested on both outdoor actual environments and indoor simulation situations. The results indicate the efficiency and effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics
Publisher	IEEE
Pages	2521-2526
ISBN (Print)	9781538637418, 9781538637425, 9781538637432
DOIs	https://doi.org/10.1109/ROBIO.2017.8324799
Publication status	Published - Dec 2017
Event	2017 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2017) - The Parisian Macao, Macao, China Duration: 5 Dec 2017 → 8 Dec 2017 http://2017.ieee-robio.org/

Conference

Conference	2017 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2017)
Place	Macao, China
Period	5/12/17 → 8/12/17
Internet address	http://2017.ieee-robio.org/

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10.1109/ROBIO.2017.8324799

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@inproceedings{b1924ee61baa4ce39f1b87465ac65ce2,

title = "Simultaneous Clustering Classification and Tracking on Point Clouds using Bayesian filter",

abstract = "Simultaneous Clustering, Classification and Tracking (SCCT) maintains many challenges, especially for point cloud data. SCCT is an essential process to facilitate the autonomous mobile systems. We present a novel unified framework from the object extraction to tracking with real-time performance. The framework can be described as five sub-tasks: ground extraction, clustering, recognition, tracking and representation. We compare the adjacent two frames to solve dense tracking and motion estimation. The state of each clustered object (moving or static) is estimated by using Spatial-Temporal methods. The distinguish objects with different features are extracted. Conditional Random Field and Bayesian filter are adopted to solve the data association problem. All the algorithmic modules have been tested on both outdoor actual environments and indoor simulation situations. The results indicate the efficiency and effectiveness of the proposed method.",

author = "Sukai Wang and Huaiyang Huang and Ming Liu",

year = "2017",

month = dec,

doi = "10.1109/ROBIO.2017.8324799",

language = "English",

isbn = "9781538637418",

pages = "2521--2526",

booktitle = "Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics",

publisher = "IEEE",

address = "United States",

note = "2017 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2017) ; Conference date: 05-12-2017 Through 08-12-2017",

url = "http://2017.ieee-robio.org/",

}

Wang, S, Huang, H & Liu, M 2017, Simultaneous Clustering Classification and Tracking on Point Clouds using Bayesian filter. in Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics. IEEE, pp. 2521-2526, 2017 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2017), Macao, China, 5/12/17. https://doi.org/10.1109/ROBIO.2017.8324799

Simultaneous Clustering Classification and Tracking on Point Clouds using Bayesian filter. / Wang, Sukai; Huang, Huaiyang; Liu, Ming.
Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics. IEEE, 2017. p. 2521-2526.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Simultaneous Clustering Classification and Tracking on Point Clouds using Bayesian filter

AU - Wang, Sukai

AU - Huang, Huaiyang

AU - Liu, Ming

PY - 2017/12

Y1 - 2017/12

N2 - Simultaneous Clustering, Classification and Tracking (SCCT) maintains many challenges, especially for point cloud data. SCCT is an essential process to facilitate the autonomous mobile systems. We present a novel unified framework from the object extraction to tracking with real-time performance. The framework can be described as five sub-tasks: ground extraction, clustering, recognition, tracking and representation. We compare the adjacent two frames to solve dense tracking and motion estimation. The state of each clustered object (moving or static) is estimated by using Spatial-Temporal methods. The distinguish objects with different features are extracted. Conditional Random Field and Bayesian filter are adopted to solve the data association problem. All the algorithmic modules have been tested on both outdoor actual environments and indoor simulation situations. The results indicate the efficiency and effectiveness of the proposed method.

AB - Simultaneous Clustering, Classification and Tracking (SCCT) maintains many challenges, especially for point cloud data. SCCT is an essential process to facilitate the autonomous mobile systems. We present a novel unified framework from the object extraction to tracking with real-time performance. The framework can be described as five sub-tasks: ground extraction, clustering, recognition, tracking and representation. We compare the adjacent two frames to solve dense tracking and motion estimation. The state of each clustered object (moving or static) is estimated by using Spatial-Temporal methods. The distinguish objects with different features are extracted. Conditional Random Field and Bayesian filter are adopted to solve the data association problem. All the algorithmic modules have been tested on both outdoor actual environments and indoor simulation situations. The results indicate the efficiency and effectiveness of the proposed method.

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DO - 10.1109/ROBIO.2017.8324799

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781538637418

SN - 9781538637425

SN - 9781538637432

SP - 2521

EP - 2526

BT - Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics

PB - IEEE

T2 - 2017 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2017)

Y2 - 5 December 2017 through 8 December 2017

ER -

Simultaneous Clustering Classification and Tracking on Point Clouds using Bayesian filter

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