Detail publikace

Video-Based Bicycle Detection in Underground Scenarios

Originální název

Video-Based Bicycle Detection in Underground Scenarios

Anglický název

Video-Based Bicycle Detection in Underground Scenarios

Jazyk

en

Originální abstrakt

Automatic surveillance systems are an important emerging application of object detection algorithms in video. The nature of such systems implies several requirements on the used algorithms. Also, searching for less usual objects (in contrast to frontal human faces, car masks, etc.) is required, such as detection of bicycles. It appears that detection of such objects cannot be solved by just applying a standard statistical or other general detector, but by constructing a specialized detector composed of several standard image processing and object-detection techniques combined together ad hoc. A detector of bicycles in video data from standard low-resolution CCTV surveillance system is presented in this contribution. Bicycle detection approach covered by this paper aims to cope with highly-noisy low-resolution data, to use simple image-processing methods and to work in real time. Although the method itself does not constitute a generally usable object detector, it covers several interesting aspects which can be re-used in tasks similar to the given one. Low-level features extracted from the video used for wheel-candidate classification are described in detail. The system is applied and evaluated on real data and the results are discussed.

Anglický abstrakt

Automatic surveillance systems are an important emerging application of object detection algorithms in video. The nature of such systems implies several requirements on the used algorithms. Also, searching for less usual objects (in contrast to frontal human faces, car masks, etc.) is required, such as detection of bicycles. It appears that detection of such objects cannot be solved by just applying a standard statistical or other general detector, but by constructing a specialized detector composed of several standard image processing and object-detection techniques combined together ad hoc. A detector of bicycles in video data from standard low-resolution CCTV surveillance system is presented in this contribution. Bicycle detection approach covered by this paper aims to cope with highly-noisy low-resolution data, to use simple image-processing methods and to work in real time. Although the method itself does not constitute a generally usable object detector, it covers several interesting aspects which can be re-used in tasks similar to the given one. Low-level features extracted from the video used for wheel-candidate classification are described in detail. The system is applied and evaluated on real data and the results are discussed.

BibTex


@inproceedings{BUT33718,
  author="Vítězslav {Beran} and Adam {Herout} and Ivo {Řezníček}",
  title="Video-Based Bicycle Detection in Underground Scenarios",
  annote="Automatic surveillance systems are an important emerging application of object
detection algorithms in video. The nature of such systems implies several
requirements on the used algorithms. Also, searching for less usual objects (in
contrast to frontal human faces, car masks, etc.) is required, such as detection
of bicycles. It appears that detection of such objects cannot be solved by just
applying a standard statistical or other general detector, but by constructing
a specialized detector composed of several standard image processing and
object-detection techniques combined together ad hoc. A detector of bicycles in
video data from standard low-resolution CCTV surveillance system is presented in
this contribution. 

Bicycle detection approach covered by this paper aims to cope with highly-noisy
low-resolution data, to use simple image-processing methods and to work in real
time. Although the method itself does not constitute a generally usable object
detector, it covers several interesting aspects which can be re-used in tasks
similar to the given one. Low-level features extracted from the video used for
wheel-candidate classification are described in detail. The system is applied and
evaluated on real data and the results are discussed.",
  address="University of West Bohemia in Pilsen",
  booktitle="Proceedings of WSCG'09",
  chapter="33718",
  edition="17-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision",
  howpublished="print",
  institution="University of West Bohemia in Pilsen",
  year="2009",
  month="march",
  pages="1--4",
  publisher="University of West Bohemia in Pilsen",
  type="conference paper"
}