| Project/Area Number |
16K12460
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Perceptual information processing
|
|---|
| Research Institution | Nagoya University |
|---|
Principal Investigator |
Kato Jien 名古屋大学, 情報科学研究科, 准教授 (70251882)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
ワン ユ 名古屋大学, 国際開発研究科, 助教 (60724169)
|
|---|
| Project Period (FY) |
2016-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Keywords | 歩行者属性の詳細認識 / 歩行者認識 / 部分と全体を着目したCNN融合 / 超解像処理 / グラフィックモデル / 姿勢推定 / CNNの融合 / 画像、文章、音声等認識 / 人工知能 |
|---|
| Outline of Final Research Achievements |
The objective of this research is to recognize fine-grained categories of person attributes from enormous surveillance videos. To achieve this goal, we focus on 4 kinds of pedestrian attributes including 14 categories. We developed five methods to enhance the accuracy of fine-grained classification, including (1) super-resolution based image processing, which helps to recover the image details; (2) patch dividing based feature extraction, which extracts features in a way that preserves the spatial layout of inputs; (3) fusing multiple CNN models, which acquires more detailed features; (4) pose-wise classifier sharing, which learns robust classifiers and makes robust predictions; and (5) graphical model based inference, which utilizes the interdependence between different subcategories to update raw estimations to better ones. We conducted experiments on a pedestrian data set, and confirmed superior performance of our approach based on these methods over the state-of-the-art.
|
