arXiv subjects

Yan Ding

Publications and source records attributed to Yan Ding.

61 records · Page 4Linked to original sources

arXiv2021

A Structure Feature Algorithm for Multi-modal Forearm Registration

Augmented reality technology based on image registration is becoming increasingly popular for the convenience of pre-surgery preparation and medical education. This paper focuses on the registration of forearm images and digital anatomical models. Due to the difference in texture features of forearm multi-modal images, this paper proposes a forearm feature representation curve (FFRC) based on structure compliant multi-modal image registration framework (FAM) for the forearm.

cs.CV↗

arXiv2021

A Global to Local Double Embedding Method for Multi-person Pose Estimation

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method.

cs.CV↗

arXiv2021

Task and Situation Structures for Service Agent Planning

Everyday tasks are characterized by their varieties and variations, and frequently are not clearly specified to service agents. This paper presents a comprehensive approach to enable a service agent to deal with everyday tasks in open, uncontrolled environments. We introduce a generic structure for representing tasks, and another structure for representing situations. Based on the two newly introduced structures, we present a methodology of situation handling that avoids hard-coding domain rules while improving the scalability of real-world task planning systems.

cs.AI↗

arXiv2020

Task-Motion Planning for Safe and Efficient Urban Driving

Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where efficiency is the main concern. At the same time, the vehicles must compute continuous trajectories to perform actions at the motion level, where safety is the most important. Task-motion planning in autonomous driving faces the problem of maximizing task-level efficiency while ensuring motion-level safety. To this end, we develop algorithm Task-Motion Planning for Urban Driving (TMPUD) that, for the first time, enables the task and motion planners to communicate about the safety level of driving behaviors. TMPUD has been evaluated using a realistic urban driving simulation platform. Results suggest that TMPUD performs significantly better than competitive baselines from the literature in efficiency, while ensuring the safety of driving behaviors.

cs.RO↗

arXiv2020

Significant reduced traffic in Beijing failed to relieve haze pollution during the COVID-19 lockdown: implications for haze mitigation

The COVID-19 outbreak greatly limited human activities and reduced primary emissions particularly from urban on-road vehicles, but coincided with Beijing experiencing pandemic haze, raising the public concerns of the validity and effectiveness of the imposed traffic policies to improve the air pollution. Here, we explored the relationship between local vehicle emissions and the winter haze in Beijing before and during the COVID-19 lockdown period based on an integrated analysis framework, which combines a real-time on-road emission inventory, in-situ air quality observations and a localized chemical transport modeling system. We found that traffic emissions decreased substantially affected by the pandemic, with a higher reduction for NOx (75.9%, 125.3 Mg/day) compared to VOCs (53.1%, 52.9 Mg/day). Unexpectedly, our results show that the imbalanced emission abatement of NOx and VOCs from vehicles led to a significant rise of the atmospheric oxidizing capacity in urban areas, but only resulting in modest increases in secondary aerosols due to the inadequate precursors. However, the enhanced oxidizing capacity in the surrounding regions greatly increased the secondary particles with relatively abundant precursors, which is mainly responsible for Beijing haze during the lockdown period. Our results indicate that the winter haze in Beijing was insensitive to the local vehicular emissions reduction due to the complicated nonlinear response of the fine particle and air pollutant emissions. We suggest mitigation policies should focus on accelerating VOC and NH3 emissions reduction and synchronously controlling regional sources to release the benefits on local traffic emission control.

physics.ao-ph↗

arXiv2020

Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.

cs.RO↗

arXiv2018

Eco-Route: Recommending Economical Driving Routes For Plug-in Hybrid Electric Vehicles

High fuel consumption cost results in drivers' economic burden. Plug-In Hybrid Electric Vehicles (PHEVs) consume two fuel sources (i.e., gasoline and electricity energy sources) with floating prices. To reduce drivers' total fuel cost, recommending economical routes to them becomes one of the effective methods. In this paper, we present a novel economical path-planning framework called Eco-Route, which consists of two phases. In the first phase, we build a driving route cost model (DRCM) for each PHEV (and driver) under the energy management strategy, based on driving condition and vehicles' parameters. In the second phase, with the real-time traffic information collected via the mobile crowdsensing manner, we are able to estimate and compare the driving cost among the shortest and the fastest routes for a given PHEV, and then recommend the driver with the more economical one. We evaluate the two-phase framework using 8 different PHEVs simulated in Matlab/Simulink, and the real-world datasets consisting of the road network, POI and GPS trajectory data generated by 559 taxis in seven days in Beijing, China. Experimental results demonstrate that the proposed model achieves good accuracy, with a mean cost error of less 8% when paths length is longer than 5 km. Moreover, users could save about 9% driving cost on average if driving along suggested routes in our case studies.

cs.CY↗