People detection in low-resolution video with non-stationary background

Jianguo Zhang; Shaogang Gong

doi:10.1016/j.imavis.2008.06.013

People detection in low-resolution video with non-stationary background

Jianguo Zhang
, Shaogang Gong

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision 63(2) (2005) 153–161].

Original language	English
Pages (from-to)	437-443
Number of pages	7
Journal	Image and Vision Computing
Volume	27
Issue number	4
DOIs	https://doi.org/10.1016/j.imavis.2008.06.013
Publication status	Published - 2009

Access to Document

10.1016/j.imavis.2008.06.013

Cite this

@article{95fe9ad95f354359b56f22092bc355a2,

title = "People detection in low-resolution video with non-stationary background",

abstract = "In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision 63(2) (2005) 153–161].",

author = "Jianguo Zhang and Shaogang Gong",

year = "2009",

doi = "10.1016/j.imavis.2008.06.013",

language = "English",

volume = "27",

pages = "437--443",

journal = "Image and Vision Computing",

issn = "0262-8856",

publisher = "Elsevier",

number = "4",

}

TY - JOUR

T1 - People detection in low-resolution video with non-stationary background

AU - Zhang, Jianguo

AU - Gong, Shaogang

PY - 2009

Y1 - 2009

N2 - In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision 63(2) (2005) 153–161].

AB - In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision 63(2) (2005) 153–161].

UR - https://www.scopus.com/pages/publications/57949114980

U2 - 10.1016/j.imavis.2008.06.013

DO - 10.1016/j.imavis.2008.06.013

M3 - Article

AN - SCOPUS:57949114980

SN - 0262-8856

VL - 27

SP - 437

EP - 443

JO - Image and Vision Computing

JF - Image and Vision Computing

IS - 4

ER -

People detection in low-resolution video with non-stationary background

Abstract

Access to Document

Other files and links

Fingerprint

Cite this