Robust visual surveillance based traffic information analysis and forewarning in urban dynamic scenes

Shao, Jie; Jia, Zhenhong; Li, Zhipeng; Liu, Fuqiang; Zhao, Jianwei; Peng, Pei-Yuan

doi:10.1109/ivs.2010.5548098

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Current researchers now are looking for dynamic case where it was involved with raining, waving leaves, moving escalator, moving of sea water, moving of river water and moving of clouds [11]. All these unnecessary moving object will be detect if used the traditional method such as frame differencing or mixture of Gaussian.…”

Section: Introductionmentioning

confidence: 99%

Moving vehicle segmentation in a dynamic background using self-adaptive kalman background method

Ahmad¹,

Saad²,

Abdullah

et al. 2011

2011 IEEE 7th International Colloquium on Signal Processing and Its Applications

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This paper introduce the adaptive kalman filter to modeling dynamic background for background subtraction. Background subtraction is a method to identify object and famous used in moving object segmentation. In this paper we also investigate a comparison study on Gaussian subtraction method, frame differencing method and approximate median method. The detection of object will be shown in the result.

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Section: Introductionmentioning

confidence: 99%

Moving vehicle segmentation in a dynamic background using self-adaptive kalman background method

Ahmad¹,

Saad²,

Abdullah

et al. 2011

2011 IEEE 7th International Colloquium on Signal Processing and Its Applications

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Research Issues in Smart Vehicles and Elderly Drivers: A Literature Review

Rhiu

Kwon

Bahn

et al. 2015

International Journal of Human-Computer Interaction

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In this paper, we reviewed both studies on general smart car technologies and HCI/HVI studies that were published in journals and conferences, so that we can identify the current status of research and suggest future research directions. Furthermore, we reviewed previous studies on elderly drivers as they could be the most vulnerable social group in terms of new technology acceptance. A total of 257 articles for HCI research and 45 articles for elderly drivers were selected and reviewed from 11,267 collected articles (from 2010 to 2014). According to the results, most articles were mainly related to safety and adaptive features (e.g., driver's state recognition, vehicle surrounding monitoring, driver action-suggestion), and infotainment research in terms of HCI (e.g., IT devices-vehicle interaction, vehicle-vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature Downloaded by [New York University] at 03:59 29 July 2015 A c c e p t e d M a n u s c r i p t 2 review and technological trends analysis based on previous technical roadmaps, from HCI/HFE perspectives, research related to 'Assistance systems', 'Physiological & mental state recognition', 'Position sensor technology', 'Behavior recognition', and 'Infotainment' were suggested to HCI/HFE researchers for the further research. In particular, HCI/HFE researchers need to focus on research on acceptable levels of automation, observing new driving behaviors, investigation of driver characteristics to develop personalized services, and new technology acceptance to develop and improve smart cars in the future.

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Adaptive Method for Segmentation of Vehicles through Local Threshold in the Gaussian Mixture Model

Lima¹,

Aires²,

Reis³

2015

2015 Brazilian Conference on Intelligent Systems (BRACIS)

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Robust visual surveillance based traffic information analysis and forewarning in urban dynamic scenes

Cited by 3 publications

References 9 publications

Moving vehicle segmentation in a dynamic background using self-adaptive kalman background method

Moving vehicle segmentation in a dynamic background using self-adaptive kalman background method

Research Issues in Smart Vehicles and Elderly Drivers: A Literature Review

Adaptive Method for Segmentation of Vehicles through Local Threshold in the Gaussian Mixture Model

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