HIGH PERFORMANCE OBJECT DETECTION BY COLLABORATIVE LEARNING OF JOINT RANKING OF GRANULES FEATURES...
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Transcript of HIGH PERFORMANCE OBJECT DETECTION BY COLLABORATIVE LEARNING OF JOINT RANKING OF GRANULES FEATURES...
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
Outline
Introduction Granules JRoG features Incremental Feature Selection Method Simulated Annealing Collaborative Learning Dynamic Search for Bayesian Combination Experiments Conclusion
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
Granules
JRoG features(Joint Ranking of Granules)
JRoG example
Distance Definition
Neighbor
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
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)
Flow Chart
Collaborative Learning
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
Dynamic search
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)
Experiment1
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
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
Experiments
Experiments
Experiments
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
Conclusion
A novel collaborative learning method Dynamic Search method for Bayesian
combination Improves efficiency and accuracy Extensive to other objects like cars and
faces