pedestrian detection: introduction
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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.
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. 1491 – 1498.
– Covariance Descriptors + BoostingO. Tuzel, F. Porikli, and P. Meer, “Pedestrian detection via classification on Riemannian manifolds,” To appear in IEEE Trans. Pattern Anal.
Machine Intell., 2008.
Pedestrian detection using covariance descriptors and boosting
rejection cascades approach
Detection on negatives increases
Casc 1 Casc 2 Casc N
Detection on positives decreases
Pedestrian detection using covariance descriptors and boosting
rejection cascades approach
Detection on negatives increases Detection on positives decreases
PerformanceCov-Desc + BoostingHOG + Ker SVMHOG + Lin SVMHOG + Boosting
each black dot is an additional cascade
Inside a cascade: the weak learners1. Add a weak learner to the cascade, that is2. Assign to it a (randomly extracted) sub-region3. Compute covariance-descriptor on this region for
each– Positive sample– Negative sample
4. Are positives and negatives “easily” separable?– No? Go to 1
– Yes? Done with this cascade
Covariance descriptors
Define an image
Region R
Extract Pixel-wise Feature 1
(e.g.: color components, luminance, gradients, …)
Extract Pixel-wise Feature 2
Extract Pixel-wise Feature N
Mean, var
Mean, var
Mean, var
Covariance CR
(NxN matrix, sym
pos def)
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
Riemannian ManifoldsIn 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.
Using this detector in real-time surveillance applications
1. 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
2. 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
2. 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
Using this detector in real-time surveillance applications
Examples
Detections using the generic classifier
Detections using the generic classifier+Perspective model
Detections using the generic classifier+Perspective model+Additional relevance feedback cascades
Videos
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