1. Fast Human Detection in Crowded Scenes by Contour Integration and Local Shape Estimation Csaba Beleznai, Horst Bischof Computer Vision and Pattern Recognition, 2009

2. Outline Introduction Outline of the detection method Shape-based detection – Contour integration by integral images – Human detection by sparse contour templates Detection using approximated Shape Context Detector combination, optimization Experiments and discussion

3. Introduction High detection rates and low false alarm rates are essential. Perform for spatially separated, unoccluded humans well, but worse for high density of humans. Shape-based detection rates drop significantly in presence of occluded humans. Motion-based detection errors become evident with increasing density of humans and clutter.

4. Introduction Lin et al. [10] propose a hierarchical contour template matching scheme combined with motion detection and human inter-occlusion analysis. Need large computation This paper combined Contour Integration and Local Shape Estimation in real time

5. Outline of the detection method

6. Contour integration by integral images Use discrete unit-integer orientations

7. Contour integration by integral images 0-45 135-180 degrees 45-135 degrees

8. Contour integration by integral images Scan the image line-by-line

9. Human detection by sparse contour templates Generating sparse contour templates – 120 pedestrian images of the INRIA dataset [5] – using PCA and 11 eigenvectors are retained explaining 95% of the total variance – Generate 30 shape sample stemplates input image is filtered along the unit-integer orientations and filter responses are thresholded to obtain edge probability maps

10. Human detection by sparse contour templates denote the locally best matching head- shoulder and full-body templates, w1 and w2 are importance weights.

11. Detection using approximated Shape Context A small set (10 images) of manually segmented binary images. 3*3 cell Background subtraction

12. Detection using approximated Shape Context where p (x |li ) denotes the learned spatial distribution of the best matching codebook entry and p (li |I) = Ct (li | I ) is its likelihood.

13. Detector combination, optimization The two detector outputs are combined in a similar manner as in [10].

14. Experiments and discussion CAVIAR dataset [1] and two of our datasets (RS-A) and(RS-B). Detecting standing person with less than 50% occlusion

15. Experiments and discussion