Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features

@inproceedings{Moutarde2008RealtimeVD,
  title={Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features},
  author={Fabien Moutarde and Bogdan Stanciulescu and Amaury Breheret},
  year={2008}
}
This paper deals with real-time visual detection, by mono-camera, of objects categories such as cars and pedestrians. We report on improvements that can be obtained for this task, in complex applications such as advanced driving assistance systems, by using new visual features as adaBoost weak classifiers. These new features, the “connected controlpoints” have recently been shown to give very good results on real-time visual rear car detection. We here report on results obtained by applying… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-6 OF 6 REFERENCES

Gavrila . “ An Experimental Study on Pedestrian Classification ”

Yotam Abramson, Bruno Steux, +5 authors M. Dariu
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2006

Combining Adaboost learning and evolutionary search to select features for real-time object detection

  • Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
  • 2004

Schapire , “ A short introduction to boosting ”

E. YoavFreundRobert
  • Journal of Japanese Society for Artificial Intelligence
  • 1999

A decisiontheoretic generalization of on - line learning and an application to boosting ” , ‘ 95 European Conference on Computational Learning Theory

Robert E. Schapire
  • 1995