A method for three-dimensional reconstruction of a train accident scene using photographs

Railway accidents that usually cause numerous property and life losses occurred in recent years all around the world. In general, resources such as financial supports and incident rescue programs are required to minimize the losses after an accident. Due to lack of comprehensive information collected from accident sites, most railway emergency management departments face a predicament in setting up rescue schemes. To tackle the issue, realistic three-dimensional virtual accident scene reconstruction technology is developed, which provides and visualises supplementary materials and information about a train accident and can offer assistance to emergency crews when making decisions. We propose a photo-based three-dimensional reconstruction framework of vehicles for measuring the positions and poses of carriages involved in an accident. We implement and examine two case studies to validate this reconstruction method, which performs well in the assigned tasks

Photo-based automatic 3D reconstruction of train accident scenes

Railway accidents place significant demands on the resources of, and support from, railway emergency management departments. Once an accident occurs, an efficient incident rescue plan needs to be delivered as early as possible to minimise the loss of life and property. However, in the railway sector, most relevant departments currently face a challenge in drawing up a rescue scheme effectively and accurately with the insufficient information collected from the scene of a train accident. To assist with the rescue planning, we propose a framework which can rapidly and automatically construct a 3D virtual scene of a train accident by utilising photos of the accident spot. The framework uses a hybrid 3D reconstruction method to extract the position and pose information of the carriages involved in an accident. It adopts a geographic information system and a 3D visualisation engine to model and display the landscapes and buildings at the site of a train accident. In order to assess and validate our prototype, we quantitatively evaluate our main algorithm and demonstrate the usage of our technology with two case studies including a simulated scene with an in-lab setting and a real train derailment scene from on-site pictures. The results of both are accoun table with high accuracy and represent the ability of timely modelling and visualisation of a train accident scene

The vortex dynamics of a Ginzburg-Landau system under pinning effect

It is proved that the vortices are attracted by impurities or inhomogeities in the superconducting materials. The strong H^1-convergence for the corresponding Ginzburg-Landau system is also proved.Comment: 23page

Pose selection for animated scenes and a case study of bas-relief generation

This paper aims to automate the process of generating a meaningful single still image from a temporal input of scene sequences. The success of our extraction relies on evaluating the optimal pose of characters selection, which should maximize the information conveyed. We define the information entropy of the still image candidates as the evaluation criteria. To validate our method and to demonstrate its effectiveness, we generated a relief (as a unique form of art creation) to narrate given temporal action scenes. A user study was conducted to experimentally compare the computer-selected poses with those selected by human participants. The results show that the proposed method can assist the selection of informative pose of character effectively

Multiparty simultaneous quantum identity authentication based on entanglement swapping

We present a multiparty simultaneous quantum identity authentication protocol based on entanglement swapping. In our protocol, the multi-user can be authenticated by a trusted third party simultaneously

Realtime Dynamic 3D Facial Reconstruction for Monocular Video In-the-Wild

With the increasing amount of videos recorded using 2D mobile cameras, the technique for recovering the 3D dynamic facial models from these monocular videos has become a necessity for many image and video editing applications. While methods based parametric 3D facial models can reconstruct the 3D shape in dynamic environment, large structural changes are ignored. Structure-from-motion methods can reconstruct these changes but assume the object to be static. To address this problem we present a novel method for realtime dynamic 3D facial tracking and reconstruction from videos captured in uncontrolled environments. Our method can track the deforming facial geometry and reconstruct external objects that protrude from the face such as glasses and hair. It also allows users to move around, perform facial expressions freely without degrading the reconstruction quality

Quantum broadcast communication

Broadcast encryption allows the sender to securely distribute his/her secret to a dynamically changing group of users over a broadcast channel. In this paper, we just consider a simple broadcast communication task in quantum scenario, which the central party broadcasts his secret to multi-receiver via quantum channel. We present three quantum broadcast communication schemes. The first scheme utilizes entanglement swapping and Greenberger-Horne-Zeilinger state to realize a task that the central party broadcasts his secret to a group of receivers who share a group key with him. In the second scheme, based on dense coding, the central party broadcasts the secret to multi-receiver who share each of their authentication key with him. The third scheme is a quantum broadcast communication scheme with quantum encryption, which the central party can broadcast the secret to any subset of the legal receivers

PCA Based Robust Motion Data Recovery.

Human motion tracking is a prevalent technique in many fields. A common difficulty encountered in motion tracking is the corrupted data is caused by detachment of markers in 3D motion data or occlusion in 2D tracking data. Most methods for missing markers problem may quickly become ineffective when gaps exist in the trajectories of multiple markers for an extended duration. In this paper, we propose the principal component eigenspace based gap filling methods that leverage a training sample set for estimation. The proposed method is especially beneficial in the scenario of motion data with less predictable or repeated movement patterns, and that of even missing entire frames within an interval of a sequence. To highlight algorithm robustness, we perform algorithms on twenty test samples for comparison. The experimental results show that our methods are numerical stable and fast to work