Slide 1

Many slides based on P. FelzenszwalbP. Felzenszwalb General object detection with deformable part-based models

Slide 2

Challenge: Generic object detection

Slide 3

Histograms of oriented gradients (HOG) Partition image into blocks at multiple scales and compute histogram of gradient orientations in each block N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005Histograms of Oriented Gradients for Human Detection 10x10 cells 20x20 cells Image credit: N. Snavely

Slide 4

Histograms of oriented gradients (HOG) Partition image into blocks at multiple scales and compute histogram of gradient orientations in each block N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005Histograms of Oriented Gradients for Human Detection Image credit: N. Snavely

Slide 5

Pedestrian detection with HOG Train a pedestrian template using a linear support vector machine N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005Histograms of Oriented Gradients for Human Detection positive training examples negative training examples

Slide 6

Pedestrian detection with HOG Train a pedestrian template using a linear support vector machine At test time, convolve feature map with template N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005Histograms of Oriented Gradients for Human Detection Template HOG feature mapDetector response map

Slide 7

Example detections [Dalal and Triggs, CVPR 2005]

Slide 8

Are we done? Single rigid template usually not enough to represent a category Many objects (e.g. humans) are articulated, or have parts that can vary in configuration Many object categories look very different from different viewpoints, or from instance to instance Slide by N. Snavely

Slide 9

Discriminative part-based models P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object Detection with Discriminatively Trained Part Based Models, PAMI 32(9), 2010Object Detection with Discriminatively Trained Part Based Models Root filter Part filters Deformation weights

Slide 10

Discriminative part-based models P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object Detection with Discriminatively Trained Part Based Models, PAMI 32(9), 2010Object Detection with Discriminatively Trained Part Based Models Multiple components

Slide 11

Discriminative part-based models P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object Detection with Discriminatively Trained Part Based Models, PAMI 32(9), 2010Object Detection with Discriminatively Trained Part Based Models

Slide 12

Object hypothesis Multiscale model: the resolution of part filters is twice the resolution of the root

Slide 13

Scoring an object hypothesis The score of a hypothesis is the sum of filter scores minus the sum of deformation costs Filters Subwindow features Deformation weights Displacements

Slide 14

Scoring an object hypothesis The score of a hypothesis is the sum of filter scores minus the sum of deformation costs Concatenation of filter and deformation weights Concatenation of subwindow features and displacements Filters Subwindow features Deformation weights Displacements

Slide 15

Detection Define the score of each root filter location as the score given the best part placements:

Slide 16

Detection Define the score of each root filter location as the score given the best part placements: Efficient computation: generalized distance transforms For each default part location, find the score of the best displacement Head filter Deformation cost

Slide 17

Detection Define the score of each root filter location as the score given the best part placements: Efficient computation: generalized distance transforms For each default part location, find the score of the best displacement Head filter responsesDistance transform Head filter

Slide 18

Detection

Slide 19

Matching result

Slide 20

Training Training data consists of images with labeled bounding boxes Need to learn the filters and deformation parameters

Slide 21

Training Our classifier has the form w are model parameters, z are latent hypotheses Latent SVM training: Initialize w and iterate: Fix w and find the best z for each training example (detection) Fix z and solve for w (standard SVM training) Issue: too many negative examples Do data mining to find hard negatives

Slide 22

Car model Component 1 Component 2

Slide 23

Car detections

Slide 24

Person model

Slide 25

Person detections

Slide 26

Cat model

Slide 27

Cat detections

Slide 28

Bottle model

Slide 29

More detections

Slide 30

Quantitative results (PASCAL 2008) 7 systems competed in the 2008 challenge Out of 20 classes, first place in 7 classes and second place in 8 classes BicyclesPersonBird Proposed approach