high performance object detection by collaborative learning of joint ranking of granules features...
TRANSCRIPT
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HIGH PERFORMANCE OBJECT DETECTION BY COLLABORATIVE LEARNING OF JOINT RANKINGOF GRANULES FEATURES
Chang Huang and Ram Nevatia
University of Southern California, Institute for Robotics and Intelligent Systems
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Outline
Introduction Granules JRoG features Incremental Feature Selection Method Simulated Annealing Collaborative Learning Dynamic Search for Bayesian Combination Experiments Conclusion
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Introduction
Detect pedestrians with part occluded people
Speed up and Accuracy up Collaborate learning of Simulated
Annealing and increment selection model
Dynamic search to improve Bayesian combination
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Granules
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JRoG features(Joint Ranking of Granules)
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JRoG example
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Distance Definition
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Neighbor
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Incremental Feature Selection method
(Z is normalization factor)
N is number of training samplesM is number of featuresTime complexity is O(M ln N) , better than O(MN) in conventional AdaBoost
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Simulated Annealing
Heuristically set N=1000 x dim(g0), r = 0.011/n Θ1=1 Θ2=8So each granule can be changed 1000 times and SA ends at temperature 0.01T0
Selection of initial temperature (T0 is critical)
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Flow Chart
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Collaborative Learning
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Joint Likelihood
F: full bodyH: head and shoulderT: torsoL: legs
Z: detection responsesS: state of multiple humans
Wu and Nevatia[19] uses Bayesian combination to deal with partial occlusions in crowded scenes
Wu and Nevatia’s search
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Dynamic search
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Experiment1
Collaborate learning CL: Jump/keep ratio = 1.0, 0.2, 0.25
Initial temp.= 0.03, JRoG # bit = 3 SL: without SA process Evaluate Score:
EER (Equal Error Rate) FPR (False Positive Rate)
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Experiment1
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Experiment2
INRIA dataset Training:
2478 positive, 1218 negative samples from dataset
24780 positive by rotating, scaling above Testing:
1128 positive, 453 negative samples from dataset
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Experiment3
ETHZ Dataset Four 640x480 videos (one for training, one
for testing) 23000 negatives from internet More than 20000 pedestrians labeled Outperform others in all three videos
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Experiments
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Experiments
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Experiments
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Computational Cost
Xeon 3GHz Takes 70ms to scan 640x480 ETHZ
images at 16 scales from 1.0 to 0.125 Training of 16-layer cascade costs 2 days
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Conclusion
A novel collaborative learning method Dynamic Search method for Bayesian
combination Improves efficiency and accuracy Extensive to other objects like cars and
faces