1 / 9

Andrew Gallagher

A comparison of object detection algorithms, Schneiderman-Kanade and Viola-Jones, using a dataset of 16 images with a wide variety of ethnicities, genders, and ages. The results show that Schneiderman-Kanade outperforms Viola-Jones in terms of higher detection rate and lower false positives.

mwinton
Download Presentation

Andrew Gallagher

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Schneiderman, H. and Kanade, T. Object Detection Using the Statistics of Parts,Viola, P. and Jones Robust Real-time Object Detection, Andrew Gallagher

  2. Dataset • 16 images. 8 from my databases, and 8 from Flickr. • Large ethnic, gender and age variety. • 98 total faces. 66 “frontal” 32 profile.

  3. Test • The Viola Jones algorithm OpenCV implementation was used. (<2 sec per image). • For Schneiderman Kanade, the www.pittpatt.com demo was used. (~10-15 seconds per image, including web transmission).

  4. Results • The Schneiderman-Kanade is very good and definitely out-performs Viola-Jones. (higher detection rate and lower FP simultaneously).

  5. Example Images SchneidermanKanade ViolaJones

  6. Example Images SchneidermanKanade ViolaJones

  7. Example Images SchneidermanKanade ViolaJones

  8. SchneidermanKanade ViolaJones

  9. Example Images SchneidermanKanade ViolaJones

More Related