Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

Abstract

In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation. In addition, these methods mainly focus on 2D object detection and cannot estimate detailed properties of objects. In this paper, we propose subcategory-aware CNNs for object detection. We introduce a novel region proposal network that uses subcategory information to guide the proposal generating process, and a new detection network for joint detection and subcategory classification. By using subcategories related to object pose, we achieve state-of-the-art performance on both detection and pose estimation on commonly used benchmarks.

Published in WACV 2017

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Going Deeper with Convolutions
• Filtered Channel Features for Pedestrian Detection

Cited by in corpus (8)

• CornerNet: Detecting Objects as Paired Keypoints
• Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
• Orthographic Feature Transform for Monocular 3D Object Detection
• SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
• MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
• Efficient Deep Neural Networks
• Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
• What Can Help Pedestrian Detection?