Pedestrian Detection and Tracking With Night Vision

Xu, Fei; Liu, Xia; Fujimura, Kikuo

doi:10.1109/tits.2004.838222

Cited by 342 publications

(220 citation statements)

References 32 publications

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“…In Liu and Fujimura (2004), strategies are to apply intensity threshold, and the motion constraint. In Xu, Liu, and Fujimura (2005), intensity thresholds are used with the combination of support-vector-machine classifier and Kalman-Filter. The sliding window approaches are more promising when the image resolution is low.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Detection Based on Multi-Block Local Binary Pattern and Biologically Inspired Feature

Halidou¹,

You²,

Bogno³

2014

CIS

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Nowadays pedestrian detection plays an important role in security and driving assistance. Detecting moving object is complex, and some of the detection methods are comparatively ineffective and slow. In relation to human detection it is very useful to combine independent information sources, such as appearance and motion. To achieve acceptable detection performance, we propose inter-frames differencing image to compute the region of interest, and MB-BIF to extract features. The MB-BIF approach combines two well-known methods, the Multi-Block Local Binary Pattern and Biologically Inspired Method. We evaluate the performance of different features descriptors on different databases, and our method shows good efficiency.

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Section: Related Workmentioning

confidence: 99%

Pedestrian Detection Based on Multi-Block Local Binary Pattern and Biologically Inspired Feature

Halidou¹,

You²,

Bogno³

2014

CIS

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show abstract

“…(a) Visible spectrum (b) Infrared , 2008), or defining a threshold based on maximum/minimum intensity values in the infrared image (Fang et al, 2004;Xu et al, 2005). This is generally followed by a process that classifies these regions as pedestrian or non-pedestrian.…”

Section: 1 Infrared For Pedestrian Detectionmentioning

confidence: 99%

“…This is generally followed by a process that classifies these regions as pedestrian or non-pedestrian. Objects can be filtered according to, for example, aspect-ratio (since pedestrians are generally expected to be taller than they are wide) (Xu et al, 2005), inertial parameters (total rotational momen tum of a candidate with respect to its centre) (Fang et al, 2004), horizontal and vertical grey-level intensity profiles (Fang et al, 2004), or, as humans display bilateral symmetry, grey-level symmetry and edge symmetry of pedestrians can also be used to segment regions of interest (Bertozzi et al, 2004). Edge density can also be analysed as pedestrians in far-IR images are usually much brighter than the background, and there can be a sharp change in image intensity at their edges (Bertozzi et al, 2004).…”

Section: 1 Infrared For Pedestrian Detectionmentioning

confidence: 99%

Trends Towards Automotive Electronic Vision Systems for Mitigation of Accidents in Safety Critical Situations

Hughes¹,

O’Malley²,

O’Cualain³

et al. 2011

New Trends and Developments in Automotive System Engineering

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“…In recent years, night vision systems, exploiting advantages and benefits of IR cameras, have gained more and more interest, for the automatic detection of pedestrians at night ( [4], [6], [86]). Liu and Fujimura proposed a technique [54] as a complementary to previous shape-based approaches [96], [62]. To detect pedestrians, they search for moving objects whose motions are not consistent with the movement of the background.…”

Section: Pedestrian Detection/trackingmentioning

confidence: 99%