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