NDLI: Multi-object detection and behavior recognition from motion 3D data

Content Provider	IEEE Xplore Digital Library
Author	Kyungnam Kim Cao, M. Rao, S. Jiejun Xu Medasani, S. Owechko, Y.
Copyright Year	2011
Description	Author affiliation: HRL Laboratories, LLC, Malibu CA 90265, USA (Kyungnam Kim; Cao, M.; Rao, S.; Jiejun Xu; Medasani, S.; Owechko, Y.)
Abstract	This paper presents methods for 3D object detection and multi-object (or multi-agent) behavior recognition using a sequence of 3D point clouds of a scene taken over time. This motion 3D data can be collected using different sensors and techniques such as flash LIDAR (Light Detection And Ranging), stereo cameras, time-of-flight cameras, or spatial phase imaging sensors. Our goal is to segment objects from the 3D point cloud data in order to construct tracks of multiple objects (i.e., persons and vehicles) and then classify the multi-object tracks as one of a set of known behaviors, such as “A person drives a car and gets out”. A track is a sequence of object locations changing over time and is the compact object-level information we use and obtain from the motion 3D data. Leveraging the rich structure of dynamic 3D data makes many visual learning problems better posed and more tractable. Our behavior recognition method is based on combining the Dynamic Time Warping-based behavior distances from the multiple object-level tracks using a normalized car-centric coordinate system to recognize the interactive behavior of those multiple objects. We apply our techniques for behavior recognition on data collected using a LIDAR sensor, with promising results.
Starting Page	37
Ending Page	42
File Size	1339621
Page Count	6
File Format	PDF
ISBN	9781457705298
ISSN	21607516
e-ISBN	9781457705304
DOI	10.1109/CVPRW.2011.5981808
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laser radar Tracking Surveillance Object detection Three dimensional displays Sensors Safety
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Spatio-temporal motion features for laser-based moving objects detection and tracking

Object tracking with 3D LIDAR via multi-task sparse learning

3D Object Recognition by Surface Registration of Interest Segments

VoxNet: A 3D Convolutional Neural Network for real-time object recognition

Intersection safety using lidar and stereo vision sensors

Monte Carlo based distance dependent Chinese restaurant process for segmentation of 3D LIDAR data using motion and spatial features

Use of contextual information by Bayesian Networks for multi-object tracking in scanning laser range data

Particles coupled with data fusion for 3D tracking

Bayesian Networks and Probabilistic Data Association Methods for Multi-Object Tracking: Application to Road Safety

Multi-object detection and behavior recognition from motion 3D data

Similar Documents

Spatio-temporal motion features for laser-based moving objects detection and tracking

Object tracking with 3D LIDAR via multi-task sparse learning

3D Object Recognition by Surface Registration of Interest Segments

VoxNet: A 3D Convolutional Neural Network for real-time object recognition

Intersection safety using lidar and stereo vision sensors

Monte Carlo based distance dependent Chinese restaurant process for segmentation of 3D LIDAR data using motion and spatial features

Use of contextual information by Bayesian Networks for multi-object tracking in scanning laser range data

Particles coupled with data fusion for 3D tracking

Bayesian Networks and Probabilistic Data Association Methods for Multi-Object Tracking: Application to Road Safety

Multi-object detection and behavior recognition from motion 3D data