79 research outputs found
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation
Collision-free space detection is a critical component of autonomous vehicle
perception. The state-of-the-art algorithms are typically based on supervised
learning. The performance of such approaches is always dependent on the quality
and amount of labeled training data. Additionally, it remains an open challenge
to train deep convolutional neural networks (DCNNs) using only a small quantity
of training samples. Therefore, this paper mainly explores an effective
training data augmentation approach that can be employed to improve the overall
DCNN performance, when additional images captured from different views are
available. Due to the fact that the pixels of the collision-free space
(generally regarded as a planar surface) between two images captured from
different views can be associated by a homography matrix, the scenario of the
target image can be transformed into the reference view. This provides a simple
but effective way of generating training data from additional multi-view
images. Extensive experimental results, conducted with six state-of-the-art
semantic segmentation DCNNs on three datasets, demonstrate the effectiveness of
our proposed training data augmentation algorithm for enhancing collision-free
space detection performance. When validated on the KITTI road benchmark, our
approach provides the best results for stereo vision-based collision-free space
detection.Comment: accepted to IEEE/ASME Transactions on Mechatronic
ATG-PVD: Ticketing Parking Violations on A Drone
In this paper, we introduce a novel suspect-and-investigate framework, which
can be easily embedded in a drone for automated parking violation detection
(PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and
accurate convolutional neural network (CNN) for unsupervised optical flow
estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and
classification; and 3) an illegally parked car (IPC) candidate investigation
module developed based on visual SLAM. The proposed framework was successfully
embedded in a drone from ATG Robotics. The experimental results demonstrate
that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art
unsupervised optical flow estimation approaches in terms of both speed and
accuracy; secondly, IPC candidates can be effectively and efficiently detected
by our proposed Flow-RCNN, with a better performance than our baseline network,
Faster-RCNN; finally, the actual IPCs can be successfully verified by our
investigation module after drone re-localization.Comment: 17 pages, 11 figures and 3 tables. This paper is accepted by ECCV
Workshops 202
Symmetric Kullback-Leibler Metric Based Tracking Behaviors for Bioinspired Robotic Eyes
A symmetric Kullback-Leibler metric based tracking system, capable of tracking moving targets, is presented for a bionic spherical parallel mechanism to minimize a tracking error function to simulate smooth pursuit of human eyes. More specifically, we propose a real-time moving target tracking algorithm which utilizes spatial histograms taking into account symmetric Kullback-Leibler metric. In the proposed algorithm, the key spatial histograms are extracted and taken into particle filtering framework. Once the target is identified, an image-based control scheme is implemented to drive bionic spherical parallel mechanism such that the identified target is to be tracked at the center of the captured images. Meanwhile, the robot motion information is fed forward to develop an adaptive smooth tracking controller inspired by the Vestibuloocular Reflex mechanism. The proposed tracking system is designed to make the robot track dynamic objects when the robot travels through transmittable terrains, especially bumpy environment. To perform bumpy-resist capability under the condition of violent attitude variation when the robot works in the bumpy environment mentioned, experimental results demonstrate the effectiveness and robustness of our bioinspired tracking system using bionic spherical parallel mechanism inspired by head-eye coordination
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