Post on 15-Feb-2016
description
Human Identity Recognition in Aerial Images
Omar OreifejRamin MehranMubarak Shah
CVPR 2010, JuneComputer Vision Lab of UCF
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
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
Challenges
• Low quality images• High pose variations• Possibility of high density crowds
• We employ a robust region-based appearance matching.
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.
Weighted Region Matching (WRM)
where P(vi) is the voter’s prior.
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.
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.
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]
i
ii xxKxf )()(ˆ
Estimate the pdf directly from the data without any assumptions about the underlying distributions.
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.
Alignment
• Find the best fit of the HST model over human blobs– we train an Active Appearance Model (AAM)
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.
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.
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
Measuring the Distance Between Blobs
Number of pixelsNumber of pixels
bin bin
• Total cost in the example : 1·1+2·2=5, EMD=5/3• For two distributions, P = {pi} and Q = {qi}
P Q
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 = {rk}– We assign a weight for every region such that the
most consistent regions are given higher weights– Use the PageRank algorithm [3]
PageRank
• Conception– Vote– based on a random walk algorithm A
B D C
PR(A) = PR(B) + PR(C) + PR(D)
VisualRank: Applying PageRank to Large-Scale Image Search,余償鑫
PageRank
A
B
D
C
VisualRank: Applying PageRank to Large-Scale Image Search,余償鑫
PageRank
VisualRank: Applying PageRank to Large-Scale Image Search,余償鑫
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 rk:
Region sizePR
the voter’s weight wi =normalized sum of weights of its regions
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.
Experiments and
Results
Experiments and
Results
Experiments and
Results