Offline Vehicle Detection at Night Using Center Surround Extremas

Kosaka, Naoya; Ogura, Ryota; Ohashi, Gosuke

doi:10.1587/transfun.e98.a.1727

Cited by 1 publication

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All types of vehicle detection methods view vehicles as the smallest unit of target detection, but regarding the vehicles themselves, they have many variable features [9,10], such as headlights, taillights, license plates, and vehicle symmetries. The organic combination of these features can often represent vehicle targets.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

Application of Image Processing and Three‐Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

Song

Wang

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the threedimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods.

show abstract

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Nighttime vehicle detection can use the center surround extreme as in [10]. In this paper, we detect headlights on the inverse projection map.…”

Section: Algorithm For Nighttime Vehicle Detectionmentioning

confidence: 99%