Shao-Wen Yang

Tubularized incised plate urethroplasty for proximal hypospadias

Objective To review our experience of using the tubularized incised plate (TIP) urethroplasty (useful in the treatment of distal hypospadias) to treat proximal hypospadias.

Connected vehicle safety science, system, and framework

In this paper, we propose a framework to develop an M2M-based (machine-to-machine) proactive driver assistance system. Unlike traditional approaches, we take the benefits of M2M in intelligent transportation system (ITS): 1) expansion of sensor coverage, 2) increase of time allowed to react, and 3) mediation of bidding for right of way, to help driver avoiding potential traffic accidents. To develop such a system, we divide it into three main parts: 1) driver behavior modeling and prediction, which collects grand driving data to learn and predict the future behaviors of drivers; 2) M2M-based neighbor map building, which includes sensing, communication, and fusion technologies to build a neighbor map, where neighbor map mentions the locations of all neighboring vehicles; 3) design of passive information visualization and proactive warning mechanism, which researches on how to provide user-needed information and warning signals to drivers without interfering their driving activities.

A Vision-Based Hierarchical Framework for Autonomous Front-Vehicle Taillights Detection and Signal Recognition

Automatically recognizing rear light signals of front vehicles can significantly improve driving safety by automatic alarm and taking actions proactively to prevent rear-end collisions and accidents. Much previous research only focuses on detecting brake signals at night. In this paper, we present the design and implementation of a robust hierarchical framework for detecting taillights of vehicles and estimating alert signals (turning and braking) in the daytime. The three-layer structure of the vision-based framework can obviously reduce both false positives and false negatives of taillight detection. Comparing to other existing work addressing nighttime detection, the proposed method is capable of recognizing taillight signals under different illumination circumstances. By carrying out contrast experiments with existing state-of-the-art methods, the results show the high detection rate of the framework in different weather conditions during the daytime.

Wearable social camera: Egocentric video summarization for social interaction

Wearable social camera is an egocentric camera that summarizes the video of the users social activities. This paper proposes a core technology of the wearable social camera: egocentric video summarization for social interaction. Different from other works of third-person action/interaction recognition in egocentric videos, which focus on distinguishing different actions, this work finds the common features among all the interactions, which is called interaction features (IF). IF of the third-person is proposed to be composed of three parts: physical information of head, body languages, and emotional expression. Furthermore, hidden Markov model (HMM) is employed to model the interaction sequences, and a summarized video is generated with hidden Markov support vector machine (HM-SVM). Experimental results with a life-log dataset show that the proposed system performs well for summarizing life-log videos.

Bridge deep learning to the physical world: An efficient method to quantize network

As better performance is achieved by deep convolutional network with more and more layers, the increasing number of weighting and bias parameters makes it only possible to be implemented on servers in cyber space but infeasible to be deployed in physical-world embedded systems because of huge storage and memory bandwidth requirements. In this paper, we proposed an efficient method to quantize the model parameters. Instead of taking the quantization process as a negative effect on precision, we regarded it as a regularize problem to prevent overfitting, and a two-stage quantization technique including soft- and hard-quantization is developed. With the help of our quantization method, not only 93.75% of the parameter memory size can be reduced by replacing the word length from 32-bit to 2-bit, but the testing accuracy after quantization is also better than previous approaches in some dataset, and the additional training overhead is only 3% of the ordinary one.

Track-Clustering Error Evaluation for Track-Based Multi-camera Tracking System Employing Human Re-identification

In this study, we present a set of new evaluation measures for the track-based multi-camera tracking (T-MCT) task leveraging the clustering measurements. We demonstrate that the proposed evaluation measures provide notable advantages over previous ones. Moreover, a distributed and online T-MCT framework is proposed, where re-identification (Re-id) is embedded in T-MCT, to confirm the validity of the proposed evaluation measures. Experimental results reveal that with the proposed evaluation measures, the performance of T-MCT can be accurately measured, which is highly correlated to the performance of Re-id. Furthermore, it is also noted that our T-MCT framework achieves competitive score on the DukeMTMC dataset when compared to the previous work that used global optimization algorithms. Both the evaluation measures and the inter-camera tracking framework are proven to be the stepping stone for multi-camera tracking.

On addressing driving inattentiveness: Robust rear light status classification using Hierarchical Matching Pursuit

Automatically recognizing rear light signals of front vehicles can significantly improve driving safety by automatic alarm and taking actions proactively to prevent collisions and accidents. Much previous research only focuses on the detection of brake signals at night. In this paper, we propose a novel and robust framework to detect rear lights of vehicles and estimate their signal states at daytime. Comparing with existing state-of-the-art works, our experimental results show the high detection rate and robustness of our system in complicated light conditions.

Predict Scooter's Stopping Event Using Smartphone as the Sensing Device

Researches show that most of deadly crashes involve one or more unsafe driving behaviors typically associated with careless driving. Many researchers try to develop intelligent transportation system (ITS) or machine learning model to detect these potential risks, to make alert, and to prevent driver from traffic accident. For example, intentionally or carelessly inappropriate stopping or not stopping a vehicle may cause traffic violation or vehicle accident. However, to the best of our knowledge so far, there exist no research of ITS dedicated to collecting scooters driving profile and improving driving safety of scooter rider, given the fact of that riding scooter is one of the most important transportation means in Taiwan - every 1.56 persons in Taiwan own a scooter. In this work, taking advantages of machine learning technique, we propose a model to predict whether scooter is going to stop or not, by collecting data of various sensors using smart phone, a popular and relative cheap device, set on the handler of scooter. Experiments shows that by carefully concerning the characteristics and tendencies differ from drivers to drivers, from locations to locations, our model can detect stop event of scooter with at most 90% accuracy, such that it can provide significant information to prevent traffic violation, ex: red-light running, or car accident.

A framework for visual fog computing

Visual data are rich, which have opened vast analytics opportunities and been widely used in many applications. However, the demanding requirements of computational resources and bandwidth have prevented the data from being useful in an economically efficient manner. A visual fog paradigm is needed for efficient processing of continuous video streams by collaboratively using things in the Internet of Video Things (IoVT), comprising edge devices, intermediate gateways, and servers on premise or in the cloud, as the computing platform. The challenges lying ahead include (1) Reusability-a reusable framework across multiple vertical applications, (2) Efficiency-the intelligence for online distributing and redistributing work-load for optimal system performance, and (3) Configurability-the user interface for (layperson) users to easily analyze the visual data as well as the corresponding metadata. This paper spells out the need of a framework for visual fog computing and suggest promising research directions towards instantiations of a visual fog computing framework.