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