1. Pedestrian Detection: introduction
Approaches
Holistic detection: use local search window that meets criterias
Part-based detection: pedestrian as a collection of parts (to be found!)
Patch-based detection: local features matched against a (learned) codebook, then voting for final detection
For a survey on the approaches see: N.Dalal “Finding people in images and videos”, PhD thesis, July 2006.

2. Holistic approaches Some remarkable pedestrian detector
Haar wavelets + SVM
P. Papageorgiou and T. Poggio, “A trainable system for object detection,” Intl. J. of Computer Vision, vol. 38, no. 1, pp. 15–33, 2000.
the popular face detector from Viola Jones (haar+adaboost face-detector) + motion cues
P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, New York, NY, volume 1, 2003, pp. 734–741.
Histogram of oriented gradients (HOG)
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, CA, volume 1, 2005, pp. 886–893.
Q. Zhu, S. Avidan, M. C. Yeh, and K. T. Cheng, “Fast human detection using a cascade of histograms of oriented gradients,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, New York, NY, volume 2, 2006, pp – 1498.
Covariance Descriptors + Boosting
O. Tuzel, F. Porikli, and P. Meer, “Pedestrian detection via classification on Riemannian manifolds,” To appear in IEEE Trans. Pattern Anal. Machine Intell., 2008.

3. Pedestrian detection using covariance descriptors and boosting
rejection cascades approach
Casc 1
Casc 2
Casc N
Detection on negatives increases 
Detection on positives decreases 

4. Pedestrian detection using covariance descriptors and boosting
rejection cascades approach
Detection on negatives increases 
Detection on positives decreases 

5. Performance each black dot is an additional cascade
Cov-Desc + Boosting
HOG + Ker SVM
HOG + Lin SVM
HOG + Boosting
each black dot is
an additional cascade

6. Inside a cascade: the weak learners
Add a weak learner to the cascade, that is
Assign to it a (randomly extracted) sub-region
Compute covariance-descriptor on this region for each
Positive sample
Negative sample
Are positives and negatives “easily” separable?
No? Go to 1
Yes? Done with this cascade

7. Covariance descriptors
Define an image
Region R
Extract Pixel-wise Feature 1
Mean, var
Covariance CR
(NxN matrix, sym pos def)
Extract Pixel-wise Feature 2
Mean, var
Extract Pixel-wise Feature N
Mean, var
(e.g.: color components,
luminance, gradients, …)

8. Covariance descriptors
PROs
Versatile and flexible (you can use the pixel-wise features most suitable for your goal)
Computed very quickly using integral images
Compact (N*(N+1)/2 independent values)
CONs
Euclidean distance is NOT appropriate over symmetric positive matrices: they lie over a Riemannian manifolds

9. Riemannian Manifolds
In order to use traditional machine learning techiniques:
Move back and forth from Riemannian manifold of sym pos def matrices to euclidean space of symmetric matrices using respectively
Exponential of Matrix
Logarithm of Matrix
 Expansive computations!
J. Jost, “Riemannian Geometry and Geometric Analysis”. Springer, fourth edition, 2005.
X. Pennec, P. Fillard, and N. Ayache, “A Riemannian framework for tensor computing,” Intl. J. of Computer Vision, vol. 66, no. 1, pp. 41–66, 2006.

10. Using this detector in real-time surveillance applications
Reduce the pedestrian search region where there is (or was) motion
Build a motion history and focus the detection search over the most recent motion but keeping an eye on oldest motion regions
Exploit the background when camera is static with implicit relevance feedback:
In many surveillance scenarios it is possible to assume that the background image does not contain humans
therefore, enrich the generic pedestrian classifier
training some additional ad-hoc and view-dependent cascades
that tackle the false-positives detected in the background
The enriched classifier is to be used in the ped-detection over the given view
Use the pedestrian detection to infer the scene perspective
False detections are very limited, and most of them are out of perspective
Therefore, having defined a perspective model, it is possible to estimate it, rejecting the outliers
Once estimated, it can reject out-of-perspective detections
@ICDSC09:
Covariance Descriptors on Moving Regions for Human Detection in Very Complex Outdoor Scenes Giovanni Gualdi, Andrea Prati and Rita Cucchiara. Univ. of Modena and Reggio Emilia

11. Using this detector in real-time surveillance applications

12. Examples Detections using the generic classifier Detections using the+
Perspective model
Detections using the
generic classifier +
Perspective model
Additional relevance feedback cascades

13. Videos