Download presentation

Presentation is loading. Please wait.

Published byKaliyah Steff Modified over 2 years ago

1
Human Identity Recognition in Aerial Images Omar Oreifej Ramin Mehran Mubarak Shah CVPR 2010, June Computer Vision Lab of UCF

2
Outline Introduction Challenges Problem Definition Weighted Region Matching (WRM) – Pre-processing steps Human Detection Blob Extraction Alignment – Measuring the Distance Between Blobs – Determining the Voter’s Weight Experiments and Results

3
Introduction Identity recognition from aerial platforms is a daunting task. – Highly variant features in different poses – Vanish details under low quality images In tracking, objects are usually considered to have small displacements between observations. – Mean Shift [4] – Kalman filter-based tracking – with long temporal gaps, all assumptions of the continuous motion models become weak

4
Challenges Low quality images High pose variations Possibility of high density crowds We employ a robust region-based appearance matching.

5
Problem Definition A user is able to identify a target person over a short period of time. Humans maintained their clothing and general appearance. We define the problem as a voter-candidate race.

6
Weighted Region Matching (WRM) where P(vi) is the voter’s prior.

7
Weighted Region Matching (WRM) Equation (1) can be rewritten in a form similar to a mixture of Gaussians: where τ is a constant parameter Provide a robust representation of the distance between every voter-candidate pair. Specify the weight of every voter.

8
Human Detection We train a SVM classifier based on the HOG descriptor [6]. 6000 positive images: – humans at different scales and poses 6000 negative examples: – the background and non-human objects Train over a subset of 9000. Validation using the rest of the dataset.

9
Blob Extraction The background regions contained in the bounding boxes do not provide any information about a specific person. Segmentation method: kernel density estimator [12, 15] Estimate the pdf directly from the data without any assumptions about the underlying distributions.

10
Alignment To eliminate the variations from camera orientation and human pose. Edge detection is noisy. A coarse alignment: – eight point head, shoulders and torso (HST) model – The model captures the basic orientation of the upper part of the body.

11
Alignment Find the best fit of the HST model over human blobs – we train an Active Appearance Model (AAM)

12
Alignment We employ to compute an affine transformation to a desired pose. Align all the blobs to the mean pose generated by the AAM training set.

13
Measuring the Distance Between Blobs Treat blob as a group of small regions of features. These features compose: – Histograms of HSV channels – The HOG descriptor We apply PCA on the feature space and extract the top 30 eigen vectors.

14
Measuring the Distance Between Blobs Using Earth Mover Distance [16, 14] (EMD) Compute the minimum cost of matching multiple regions. Having each region represented as a distribution in the feature space

15
Measuring the Distance Between Blobs Number of pixels bin Total cost in the example : 1·1+2·2=5, EMD=5/3 For two distributions, P = {pi} and Q = {qi} P Q

16
Determining the Voter’s Weight We rank the collection of input images according to the value of information. Given the set of regions from all voters, R = {r k } – We assign a weight for every region such that the most consistent regions are given higher weights – Use the PageRank algorithm [3]

17
PageRank Conception – Vote – based on a random walk algorithm A BDC PR(A) = PR(B) + PR(C) + PR(D) VisualRank: Applying PageRank to Large-Scale Image Search, 余償鑫

18
PageRank A B D C VisualRank: Applying PageRank to Large-Scale Image Search, 余償鑫

19
PageRank VisualRank: Applying PageRank to Large-Scale Image Search, 余償鑫

20
In G, we connect every region from voter i to the K nearest neighbor regions of voter j where i != j. The final weight for a region r k : Region sizePR the voter’s weight w i = normalized sum of weights of its regions

21
Matching Substituting the distances and the weights in equation 2, we compute a probability for every candidate to belong to the target. The best match should be the candidate with the highest probability.

22
Experiments and Results

Similar presentations

OK

1 Bilinear Classifiers for Visual Recognition Computational Vision Lab. University of California Irvine To be presented in NIPS 2009 Hamed Pirsiavash Deva.

1 Bilinear Classifiers for Visual Recognition Computational Vision Lab. University of California Irvine To be presented in NIPS 2009 Hamed Pirsiavash Deva.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on motivation for college students Best ppt on body language Ppt on systematic layout planning Ppt on environment save energy Ppt on power transmission lines Ppt on brain tumor segmentation Ppt on flora and fauna of kerala Ppt on communication skills in hindi Ppt on indian defence forces Ppt on human nutrition and digestion for kids