2nd Online Computer Vision and Artificial Intelligence Workshop (OnCV&AI)
August 16th – August 20th, 2021, Tainan, Taiwan
We sincerely invite anyone in the world interested in spatial AI to participate. The workshop is held online and is free for everyone.
Sponsors
Program
Speakers
Live Stream
Recorded Talks
About
Our Sponsors
Time Zone
All times are listed in UTC+8, i.e. China Standard Time (CST) or Taipei Time, which is 8 hours ahead of Coordinated Universal Time (UTC).
Time Zone
All times are listed in UTC+8, i.e. China Standard Time (CST) or Taipei Time, which is 8 hours ahead of Coordinated Universal Time (UTC).
About the Workshop
This workshop is part of a series created for Ph.D. students, and other skilled engineers, to explore online computer vision and artificial intelligence techniques on the OAK-D camera. It consist of a few seminar speeches and a hackathon, distributed over a week.
During the hackathon everything is done online and implemented in real-time by our hackers on our OAK-D cameras as suggested by the audience. Everything is streamed on YouTube and Twitch.
Program
16
August
16:00 – 17:00
Jarmo Alander,
Professor of production automation at University of Vaasa
Interactive evolutionary design of image processing pipeline on an FPGA for a spectral camera
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17
August
13:00 – 14:00
Kelvin Liusiani,
Computer Vision Engineer at VOOX
Building AI Solution using Edge Computing? Why not? Person Detection on Intel NUC with OpenVINO
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18
August
15:00 – 16:00
Lee Aing,
National Chung Cheng University
Deep Neural Networks on Pose Estimation
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19
August
16:00 – 17:00
Niall O’ Mahony,
Institute of Technology Tralee/Lero – the Science Foundation Ireland Research Centre for Software
Types of Machine Learning Algorithms
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20
August
10:00 – 12:00
2nd OnCV&AI Hackathon,
National Cheng Kung University
Interactive live exploration of spatial AI applications on the OAK-D camera based on suggestions from the speakers and audience.
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Subscribe to our workshop calendar
Speakers & Hackathon
Jarmo T. Alander,
Professor of production automation at University of Vaasa in Finland
Interactive evolutionary design of image processing pipeline on an FPGA for a spectral camera
August 16th, 16:00
Bio: My interests include development of computational methods for automation, mainly for the design, digital signal processing, and optimisation with evolutionary methods like Genetic Algorithms and Genetic Programming for processing sensor data for
measurements and further control of automated machines. Since autumn 1993 I have been the professor of production automation at University of Vaasa. My study background includes physics (mainly optics and molecular physics, University of Helsinki) and computer science (robotics, Helsinki University of Technology: now known as Aalto University).
My hobbies include entomology, like observing and photographing butterflies and wasps. BTW, I will show some image processing examples using butterfly and wasp images. I am one of the founding members of the Finnish Artificial Intelligence Society (FAIS) and Finnish Pattern Recognition Society.
Abstract: High performance energy efficient computing in mobile devices is possible by using pipeline hardware architecture. In our project we are designing an FPGA based implementation of spectral camera image processing. The spectral camera is based on a Fabry-Perot interference filter and a color sensor. The camera (prototype) has been used mainly for remote sensing like satellite imaging and 3D (subcutaneous topography) medical imaging applications like melanoma diagnostics.
In this report we are describing the genetic algorithm based interactive tools to aid digital image filter design for the device. The interactive tools implemented using JavaScript are available on web. This report gives some preliminary results obtained with the tools.
Kelvin Liusiani,
Computer Vision Engineer at VOOX in Singapore
Building AI Solution using Edge Computing? Why not?
Person Detection on Intel NUC with OpenVINO
August 17th, 13:00
Bio: I am a Computer Vision Engineer at VOOX (Singapore Start Up Company), previously working as an AI Engineer at Nodeflux Indonesia.
My job is to create AI product solutions for helping people in some areas. I also have some experiences on building some AI Solutions on Surveillance: People Counting, Vehicle Counting, Face Recognition for Attendance System, Indonesian License Plate Recognition, Smart Digital Signage System, Face-Mask Detection and Social Distancing Alarm. Currently I am working on 3D image processing and AI for dimension measurement.
Abstract: Nowadays, AI has been proven to help humans. One of them is in the field of surveillance. For example in Indonesia, we have implemented AI in monitoring several things: vehicles counting, people counting, searching and recognizing faces, and during this pandemic, we also used AI to monitor people who don’t use face-masks in public areas etc. Some of these solutions usually use a centralized system, where the server is a very high computational resource.
However in some cases, we also have to apply it as an edge solution in order to reduce the costs and to cover a wider monitoring area.This time I will share my experience in building people counting solutions using Intel NUC and utilize Intel OpenVINO as AI inference with proper FPS performance.
Lee Aing,
National Chung Cheng University in Taiwan
Deep Neural Networks on Pose Estimation
August 18th, 15:00
Bio: LEE AING received the B.E. degree in electrical and energy engineering department, in the division of electronic, automation, and telecommunication, in Institute of Technology of Cambodia, Cambodia, in 2014, and the M.E. degree of information and communication technology for embedded systems in Thammasat University, Thailand, in 2017.
He is currently pursuing the PhD degree in the department of electrical engineering, National Chung Cheng University, Taiwan, under the supervision of Prof. Wen-Nung Lie. His research interests include image processing, computer vision, machine learning, and IoT for real applications.
Abstract: We are living with high technologies and among those technologies, the deep neural network plays an important role to solve many complicated tasks which had never been done before. The deep neural network is a method which can understand and figure out the problems the same as humans do. There are variety of domains that the deep neural network can be carried out. In those domains, many tasks can be performed such as 2D/3D object detection, semantic segmentation, pose estimation, etc. Among them, pose estimation seems to be given more attention in recent years. This is because it can be applied to many real-world applications such as robot grasping, self-driving car, etc. Normally, “pose estimation” refers to the estimation of the orientation (rotation) and position (translation) of the targeted objects.
In this talk, we will introduce four popular sub-fields: 1) 2D/3D human (skeleton) pose estimation, 2) head pose estimation, 3) 6DoF object pose estimation, and 4) 7D vehicle pose estimation. Definitions, state-of-the-art methods by deep neural networks, constraints and practical implementations, and future vision, will be described and demonstrated.
Niall O’ Mahony,
Institute of Technology Tralee/Lero – the Science Foundation Ireland Research Centre for Software in Ireland
Types of Machine Learning Algorithms
August 19th, 16:00
Bio: Mr. Niall O’ Mahony is actively working on a PhD on the development of computer vision and artificial intelligence in the field of agritech, addressing problems relating to animal health monitoring.
Niall has worked within IMaR Research Centre at Munster Technological University since 2015 and within Lero Research Centre since 2017. His research experience includes sensors systems, embedded systems, robotics and artificial intelligence. He holds a Master’s degree and a bachelors in Mechatronic Engineering.
Abstract: In this extensive introductory lecture to machine learning, we will overview the different types of algorithms from the elementary machine learning for solving simple problems through to more advanced deep learning algorithms. The algorithms we will examine include logistic regression, Support Vector Machines, Decision Trees, Artificial Neural Networks, Convolutional Neural Networks, Genetic Algorithms, Reinforcement Learning and K-means clustering. This lecture will not get too deep into the mathematics of each algorithm but rather will offer an intuitive understanding of different types of loss functions and training paradigms.
Hackathon
August 20th, 10:00 – 12:00
Interactive live exploration of spatial AI applications on the OAK-D camera based on suggestions from the speakers and audience. This is conducted by our hackers: Jose Ramon Chang, Jacob Chen, and Ric Tu, with Rain Wu handling communication with the audience.
Follow the Live Stream
Everything is streamed in English on YouTube and Twitch. Join by clicking on the logo of your preferred service.
Recorded Talks & Hackathon
Jarmo T. Alander,
Professor of production automation at University of Vaasa
Interactive evolutionary design of image processing pipeline on an FPGA for a spectral camera
August 16th,
16:00
Kelvin Liusiani,
Computer Vision Engineer at VOOX
Building AI Solution using Edge Computing? Why not?
Person Detection on Intel NUC with OpenVINO
August 17th,
13:00
Lee Aing,
National Chung Cheng University
Deep Neural Networks on Pose Estimation
August 18th,
15:00
Niall O’ Mahony,
Institute of Technology Tralee/Lero – the Science Foundation Ireland Research Centre for Software
Types of Machine Learning Algorithms
August 19th,
16:00
Hackathon,
Interactive live exploration of spatial AI applications on the OAK-D camera based on suggestions from the speakers and audience.
This is conducted by our hackers: Jose Ramon Chang, Jacob Chen, and Ric Tu, with Rain Wu handling communication with the audience.
August 20th,
10:00
Past & Upcoming Workshops
About the Team
This workshop is arranged by the Nordling Lab located at the Dept. of Mechanical Engineering, National Cheng Kung University. It is sponsored by the National Cheng Kung University in Taiwan. We are part of the “National Cheng Kung University‘s Parkinson’s Disease Quantifiers” team lead by Ass. Prof. Torbjörn Nordling and Asc. Prof. Chi-Lun Lin selected as a finalist in the OpenCV AI competition 2021 out of more than 1400 teams globally.
The team consists of Dr. Akram Ashyani, Jose Chang, Esteban Roman, Tachyon Kuo, Gavin Vivaldy, Jacob Chen, Yushan Lin, Ric Tu, Prof. Chi-Lun Lin, and Prof. Torbjörn Nordling. We are working on quantification of motor degradation in Parkinson’s disease together with Prof. Chun-Hsiang Tan at Kaohsiung Medical University, and Dr. Chad Tsung-Lin Lee and Dr. Chung-Yao Chien at National Cheng Kung University Hospital. More precisely, on analysing the use of micro motions for assessment of motor degradation based on the Unified Parkinson’s Disease Rating Scale (UPDRS).
The National Cheng Kung University‘s Parkinson’s Disease Quantifiers team
About OAK-D
Luxonis and OpenCV AI Kit with Depth (OAK-D) has won the Best Camera and Sensor product of the year award by the Edge AI and Vision Alliance.
Luxonis is the company that has developed the OpenCV AI Kit. We received these cameras as finalists in the OpenCV AI competition 2021.
About NCKU
The National Cheng Kung University (NCKU) is one of the two major comprehensive universities in Taiwan with app. 22 000 full-time students, attracting some of the best students in Taiwan. NCKU is number one in Academia-Industry Collaborations nationwide. Times Higher Education placed NCKU 38th in Impact Rankings 2020, 103rd in Asia University Rankings 2020. Academic Ranking of World Universities placed NCKU 301-400th internationally.
About OpenCV
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies.
Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan.
It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.
About AIA
Taiwan Artificial Intelligence Association (AIA) was founded in 2020, a leading purpose-driven organization for promoting commercialization of AI application, as well as advanced AI digital transformation for national and global industries.
About Crowdhelix
Crowdhelix is an Open Innovation platform that forges links between an international network of excellent researchers and innovating companies, so that they can plan and deliver pioneering collaborative projects Crowdhelix is open to applications from any organisation, of any size, anywhere in the world, that can demonstrate a strategic commitment to collaborative research and innovation. The network’s main focus is the European Union’s Horizon Europe programme, which has a €95.5 billion budget that funds thousands of collaborative research and innovation projects worldwide. Our goal is to connect leading research institutions and innovative companies around the world, so that together they can plan and deliver pioneering Horizon Europe projects.
About Luxonis
Our mission is to improve the engineering efficiency of embedding performant, spatial AI + CV into products. We do this by building and maintaining the open-source DepthAI ecosystem (more) which is also now the OpenCV AI Kit.
In other words, we build the core technology that allows human-like perception in real products – allowing 0-to-1 applications in nearly every industry. The technology allows solving problems that need human-like perception, but say in a 1″ cube. A good external writeup about Luxonis is on Bloomberg.
We are enabling what was science-fiction as of 2017:
- Wearable devices that perceive the world and allow the blind to perceive through soundscapes
- Embedded systems that can automatically protect endangered species
- Robots that enable organic farming with zero chemicals (by using lasers to target weeds and pests)
- Perception built into heavy machinery to real-time protect the health and safety of workers.
- Perception built into remote areas to autonomously monitor and protect the environment from leaks and other hazardous conditions.
- New forms of communication devices – bridging the gap between the in-person experience and the Zoom experience.
The above are just quick examples. There are countless solutions to fascinating problems being built off of our platform, with 15,000 devices shipped to date, in the hands of thousands of companies, some of which are working to integrate our technology into their own products.
We are a small startup with aims to have an outsized impact on the world. Still in the earliest stages, the solutions built with our technology have been covered in every top news, tech, and science publication in the world (Forbes, WSJ, Bloomberg, Washington Post, Engadget, etc.). We hold the records (independently) for the largest KickStarter raise for a computer vision project, for an AI project, and for a printed circuit board project.
Our mission is to materially improve the engineering efficiency of the world. This is what our team (and backers) previously did in another market (in business WiFi, with UniFi). And we now aim to do the same for this super-power of human-like perception in embedded systems – making it so prototyping with this power takes hours, and implementing it into a product is as fast as an enclosure can be designed and produced.
We are currently hiring:
Register Now!
We sincerely invite anyone in the world interested in spatial AI to participate. The workshop is held online and is free for everyone.
Organizer: The Nordling Lab @ National Cheng Kung University in Taiwan
Honorary Chair: Chair Prof. Cheng-Wen Wu
General Chair: Ass. Prof. Torbjörn Nordling
Technical Chair: Asc. Prof. Chi-Lun Lin
Assisting Chair: Dr. Akram Ashyani
Speaker Coordinators: Esteban Roman, Ray Chen
Lead Hacker: Jose Ramon Chang
Assisting Hackers: Jacob Chen, Ric Tu
Chat Host: Rain Wu
Streamer: Austin Su
Camera Operators: Ric Tu, YuShan Lin
Photographer: Gavin Vivaldy
Designer: Victor Osorio
Administrators: Winnie Tu, Anna Chu