HIGH PERFORMANCE OBJECT DETECTION BY COLLABORATIVE LEARNING OF JOINT RANKING OF 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 samples M is number of features Time complexity is O(M ln N), better than O(MN) in conventional AdaBoost

Simulated Annealing Heuristically set N=1000 x dim(g 0 ), r = /n Θ 1 =1 Θ 2 =8 So each granule can be changed 1000 times and SA ends at temperature 0.01T 0 Selection of initial temperature (T 0 is critical)

Flow Chart

Collaborative Learning

Joint Likelihood F: full body H: head and shoulder T: torso L: legs Z: detection responses S: 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 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)  negatives from internet  More than pedestrians labeled  Outperform others in all three videos

Experiments

Computational Cost  Xeon 3GHz  Takes 70ms to scan 640x480 ETHZ images at 16 scales from 1.0 to  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