I. INTRODUCTION Sparse representation-based classification (SRC) has re-

cently attracted substantial research attention However, the computational complexity of testing makes it

challenging to deploy SRC in practice We propose a novel method for human action recognition,

leveraging coarse-to-fine sparse representations that have been obtained through dictionary sub-sampling

The proposed method reduces the time complexity of test-ing at no substantial loss in recognition accuracy

JongHo Leea, Hyun-seok Mina, Jeong-jik Seoa, Wesley De Nevea,b, and Yong Man Roa

aImage and Video Systems Lab, KAIST, Republic of KoreabMultimedia Lab, Ghent University-iMinds, Belgium

website: http://ivylab.kaist.ac.kr

IEEE International Conference on Multimedia & Expo (ICME), July 2014, Chengdu, China

SUB-SAMPLED DICTIONARIES FOR COARSE-TO-FINE SPARSE REPRESENTATION-BASED HUMAN ACTION RECOGNITION

e-mail: ymro@ee.kaist.ac.kr

II. PROPOSED APPROACH 1. Training

Fig. 2. Time complexity of different human action recognition approaches.

Fig. 1. Accuracy of different human action recognition approaches.

0

10

20

30

40

50

60

70

150 300 450 600 750 900 1050 1200 1350 1500

Tim

e co

mp

lexi

ty(s

)

Number of atoms(ls)

Proposed method with ds =48

Proposed method with ds =72

Proposed method with ds =144

Conventional method

0.76

0.78

0.8

0.82

0.84

0.86

0.88

0.9

150 300 450 600 750 900 1050 1200 1350 1500

Rec

og

nit

ion

acc

ura

cy

Number of atoms(ls)

Proposed method with ds =48Proposed method with ds =72Proposed method with ds =144Conventional method

III. EXPERIMENTS 1. Experimental setupDataset: UCF-50Feature: Cuboid detector + HOG descriptorHomotopy-based 1-norm minimization

2. Experimental resultsConventional method: classification only uses the FGD

IV. CONCLUSIONS We proposed a novel method for human action recognition

using coarse-to-fine sparse representations The proposed method achieves efficient human action

recognition at no substantial loss in recognition accuracy

2. Testing

Y

Random projection

Feature Extraction

Test video clip

…Class 1 Class 2

Φ𝑠 ,1Φ𝑠 ,2Φ𝑠 ,3 Φ𝑠 ,𝐾

Sparse Coefficients

Ranking 1 +1 +4 𝑯Candidate

Actions

Candidate Action Selection

Coarse-Grained Dictionary (CGD)

O X X O

We select candidate actions

Feature Extraction

…Action 1 Action 2 Action 3 Action

Training Dataset

Action 1 Action 2 Action 3 Action

… … … …

…

Action 1Action 2Action 3 Action

Fine-Grained Dictionary

(FGD)

Coarse-Grained Dictionary (CGD)

Φ𝑠 ,1Φ𝑠 ,2Φ𝑠 ,3 Φ𝑠 ,𝐾

Φ𝑜 ,1 Φ𝑜 , 2 Φ𝑜 , 3 Φ𝑜 ,𝐾

Random projection (for reducing the dimension of the atoms) Random sampling (for reducing the number of atoms)

Dictionary Construction

Action 1 Action 2 Action 3 Action

…

…

Pruned FGD

Φ𝑜 ,1 Φ𝑜 , 2 Φ𝑜 , 3 Φ𝑜 ,𝐾

Action 1 Action 2 Action 3 Action

…

Candidate

Actions

O X X O

Φ𝑝𝑟 ,1

Action 1

Φ𝑝𝑟 ,𝐻

Action

… Pruned FGD𝐃𝑝𝑟

Classification We can find the sparse representation of with

We label with the action that comes with the smallest resid-ual error

is a new vector whose only nonzero entries are the entries in associated with the action

Φ𝑜 ,1 Φ𝑜 , 2 Φ𝑜 , 3 Φ𝑜 ,𝐾