Detecting People in Images: An Edge Density Approach

In this paper, we present a new method for detecting visual objects in digital images and video. The novelty of the proposed method is that it differentiates objects from non-objects using image edge characteristics. Our approach is based on a fast object detection method developed by Viola and Jones. While Viola and Jones use Harr-like features, we propose a new image feature - the edge density - that can be computed more efficiently. When applied to the problem of detecting people and pedestrians in images, the new feature shows a very good discriminative capability compared to the Harr-like features.

[1]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.

[2]  Michel Desvignes,et al.  Human model for people detection in dynamic scenes , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[3]  Pradeep K. Khosla,et al.  Learning to detect partially labeled people , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[4]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[6]  Tomaso A. Poggio,et al.  Trainable pedestrian detection , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[7]  Takeo Kanade,et al.  People detection and tracking in high resolution panoramic video mosaic , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[8]  I. Haritaoglu,et al.  Attentive billboards , 2001, Proceedings 11th International Conference on Image Analysis and Processing.

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.