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Pedestrian Recognition from a Moving Catadioptric Camera

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 4713)

Abstract

This paper presents a real-time system for vision-based pedestrian recognition from a moving vehicle-mounted catadioptric camera. For efficiency, a rectification of the catadioptric image using a virtual cylindrical camera is employed. We propose a novel hybrid combination of a boosted cascade of wavelet-based classifiers with a subsequent texture-based neural network involving adaptive local features as final cascade stage. Within this framework, both fast object detection and powerful object classification are combined to increase the robustness of the recognition system. Further, we compare the hybrid cascade framework to a state-of-the-art multi-cue pedestrian recognition system utilizing shape and texture cues. Image distortions of the objects of interest due to the virtual cylindrical camera transformation are both explicitly and implicitly addressed by shape transformations and machine learning techniques. In extensive experiments, both systems under consideration are evaluated on a real-world urban traffic dataset. Results show the contributions of the various components in isolation and document superior performance of the proposed hybrid cascade system.

Keywords

  • Pedestrian Detection
  • Slide Window Approach
  • Camera Setup
  • Cascade Stage
  • Shape Template

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-74936-3_46
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© 2007 Springer-Verlag Berlin Heidelberg

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Schulz, W., Enzweiler, M., Ehlgen, T. (2007). Pedestrian Recognition from a Moving Catadioptric Camera. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_46

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  • DOI: https://doi.org/10.1007/978-3-540-74936-3_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74933-2

  • Online ISBN: 978-3-540-74936-3

  • eBook Packages: Computer ScienceComputer Science (R0)