The KITTI Vision Benchmark Suite

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 work, 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.

Due to intellectual property, we cannot release the code for
now, will make the code available when it is possible.

@inproceedings{xiang2017subcategory,
author = {Xiang, Yu and Choi, Wongun and Lin, Yuanqing
and Savarese, Silvio},
title = {Subcategory-aware Convolutional Neural
Networks for Object Proposals and Detection},
booktitle = {IEEE Winter Conference on Applications of
Computer Vision (WACV)},
year = {2017}
}