A Tutorial on HOG Human Detection Yen-Chun Chen

They use HOG human detector So far we have seen... Action detection Deformable Part-Based Model They use HOG human detector

Histograms of Oriented Gradients Dalal and Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

Challenges in person detection Different poses Variable appearance/clothing Other difficulties common in general object detections illumination background clutter

Overview 64 x 128 detection window 8 x 8 cell

Gradient histogram 8 x 8 cell -- 64 gradients 9-bin histogram 85 3/4 1/4 3/4

Block Normalization Increase robustness to contrast, illumination changes Group cells into overlapping blocks normalize the block as 36-d (4 histograms x 9 bins) vector

Final Descriptor and Detection 64 x 128 window -- 7 x 15 blocks 7 x 15 blocks x 4 cells x 9-bin histogram = 3780-d feature vectors Use linear SVM to classify person/ non-person windows Use sliding window in detection

Engineering the Feature

Color space

Color space different color spaces don’t affect much color better than greyscale

Gradients Gaussian smoothing Different derivative masks no smoothing !! the information of the image is from abrupt edges at fine scale

Spatial/ Orientation Binning 9-bin works the best signed/ unsigned gradient? (360 degrees vs 180 degrees) The clothing of human and the background varies a lot Make the signs of contrast uninformative

Evaluation Metric Detection, a single accuracy value doesn’t make sense Low threshold- detect more people but many false positives High threshold- fewer false positives but doesn’t detect all people Detection Error Tradeoff (DET) Curves Miss Rate = (false negative) / (true positive - false negative) FPPW (False Positive Per Window) : (false positive) / (total # of negative training sample)

The Rest faster computation

Discussion Normalization local variations in illumination and foreground-background contrast make gradients vary a lot overlap significantly improves the performance

Most important cells are on contours of human shape Gradients inside the person are negative cues

Demo