A New Concept of Drive Recording System by Estimating Human Intention Using Particle Filters

Ikoma, Norikazu; Kawamoto, Kazuhiko; Kawano, Hideaki; Maeda, Hiroshi; Matsuda, Kohei; Akahoshi, K.

doi:10.1109/icicic.2007.47

Cited by 5 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But all the abovementioned methods just take into account the driver factor except for [47] which took into account the driver and vehicle factors. Two factors were also incorporated in [48], the driver behaviour and environment, to estimate the driver's intention. They matched outside vehicles and inside driver's head direction to determine whether the driver has noticed the outside vehicle or not.…”

Section: Methods Based On Stereo Cameramentioning

confidence: 99%

Driver inattention monitoring system for intelligent vehicles: A review

Dong

Uchimura

et al. 2009

2009 IEEE Intelligent Vehicles Symposium

126

157

View full text Add to dashboard Cite

This paper gives a review ofthe literature on driver inattention monitoring system for the purpose of active safe driving. In this paper driving inattention is classified into two categories: fatigue and distraction, while fatigue and distraction can also contain many types and levels. Individual difference on inattention phenomenon makes it more complicated to correctly detect and recognize driving inattention. Driver attention monitoring has been intensively researched in recent years and many approaches have been proposed, which include biological signal (EEG, ECG, EOG and sEMG) processing method, subjective report method, and behavior analysis method. This survey reviews a number of promising approaches and provides an overview of recent developments in this domain. The emphasis ofthis paper is to discuss the various methodologies to monitor driving inattention. We conclude with some thoughts about future directions.

show abstract

Section: Methods Based On Stereo Cameramentioning

confidence: 99%

Driver inattention monitoring system for intelligent vehicles: A review

Dong

Uchimura

et al. 2009

2009 IEEE Intelligent Vehicles Symposium

126

157

View full text Add to dashboard Cite

show abstract

“…Hierarchical genetic particle filters are a new variation for performing articulated human motion, and the technique has been proposed by [40]. In addition, [41] has proposed a new concept of a drive recording system using a particle filter. This technique has been used to track the face of a driver in order to estimate human intention surrounding a car.…”

Section: A Particle Filtermentioning

confidence: 99%

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Musa

Salleh

Bakar

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

“…To reflect car driver's intention for the safety support is important for the system to be succeeded, i.e., accepted by a car driver and efficiently used based on communication between the car driver and the system [1]. In order to know the car driver's intention by the system, it is necessary to observe car driver's behavior by some method not disturbing car driving itself without having any special equipments on the body such as markers [2].…”

Section: Introductionmentioning

confidence: 99%

On GPGPU parallel implementation of hands and arms motion estimation of a car driver with depth image sensor by particle filter

Ikoma

2014

2014 World Automation Congress (WAC)

Self Cite

View full text Add to dashboard Cite

GPGPU parallel computation technology has been combined with depth image sensor such as Microsoft Xbox360 KINECT for real-time estimation of car driver's hands and arms motion with an elaborated tracking method based on particle filter. Vision observation by KINECT including depth image provides more accurate hand/arm region information, so we can extend the motion estimation method, not only on hands/wrists region with skin color cue, but also on arms region not necessarily having skin color, based on a depth signal. In addition, with particle filter for state estimation in robust and in sequentially with GPGPU parallel implementation for real-time computation, it allows us to develop a real-time motion estimation system of a car driver. Contribution of this paper is twofold; 1) to provide whole summary of steering hands / arms motion estimation methods so far based on particle filters and partially with the aid of GPGPU technology, and 2) to propose a new system implementation of GPGPU parallel particle filter not only for hands/wrists region but also for arms region of a car driver with the aid of depth image sensor. Some experimental results have been shown with the proposed implementation.

show abstract

A New Concept of Drive Recording System by Estimating Human Intention Using Particle Filters

Cited by 5 publications

References 5 publications

Driver inattention monitoring system for intelligent vehicles: A review

Driver inattention monitoring system for intelligent vehicles: A review

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

On GPGPU parallel implementation of hands and arms motion estimation of a car driver with depth image sensor by particle filter

Contact Info

Product

Resources

About