최근에 개발 적용된 DEEP LEARNING 기반의 HUMAN DETECTOR TEST 영상입니다.
국내에서 Deep learning 기술을 활용하요 CCTV 분야의 기술을 개척하는 회사 소식이 반갑습니다.
대구일보에 소개된 신생 업체 소식 입니다.
기사(대구일보) : http://www.idaegu.com/?c=5&uid=371048
TOKYO (AP) — Masayoshi Son, chief executive of SoftBank Group Corp., says artificial intelligence combined with data gathered by billions of sensors will bring on an "information revolution," that will benefit people more than the 19th Century Industrial Revolution.
AI deep learning expert and University of Montreal Professor Yoshua Bengio talks about deep learning—what Deep Learning and AI are, how Deep Learning got there, where it’s going, and how you can learn more about it. He discusses the latest in neural nets, unsupervised learning, generative adversarial networks, soft attention, optimization, and more.
Yoshua Bengio (born 1964 in France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. Yoshua Bengio is Full Professor of the Department of Computer Science and Operations Research, head of the Montreal Institute for Learning Algorithms (MILA), CIFAR Program co-director of the CIFAR program on Learning in Machines and Brains, Canada Research Chair in Statistical Learning Algorithms. His main research ambition is to understand principles of learning that yield intelligence. He teaches a graduate course in Machine Learning and supervises a large group of graduate students and post-docs. His research is one of the most cited scientists in the field of deep learning.
The video analytics industry is still shaking off the reputational damage that it sustained as a result of promising too much and delivering too little in the past. For years, the reliability of video analytics has been extremely variable, with vendors struggling to develop algorithms that could function in complex scenes. The industry has come a long way in recent years, and the capabilities of more conventional video analytics have steadily increased. However, deep-learning analytics are poised to revolutionise the industry, and facilitate a leap in the capabilities of video analytics. In the last couple of years, there has been a marked increase in research and development in deep-learning neural networks, proving their capabilities, generating considerable excitement, and putting them within reach of a much wider user group.
Deep learning appears to be able to offer a level of accuracy and reliability in object and behaviour classification that not only enables video analytics finally to deliver on some of the lofty but as yet unrealised claims made in the past, but pushes capabilities far beyond them. Broadly speaking, there are two main areas in which deep learning analytics offer great benefits over the technology that has preceded it. They are:
A long-held complaint levied against traditional analytics products was that their algorithms were unable to distinguish between objects and behaviours that a human being would have no problem classifying. This deficiency in computer vision algorithms results either in missed security breaches or false alarms. The ability of deep learning algorithms to view a scene intuitively, as a human viewer would, means that detection accuracy increases dramatically, while false alarm rates fall. Neural networks allow a computer to apply a series of assessments to a given situation. This is an important development for the video analytics industry. Although some end-users may not need an analytics solution that is 100% accurate 100% of the time, many use cases require that their security system be as close to infallible as possible. Users in the critical infrastructure sector, for instance, cannot afford to miss a breach in their security; and can spend a large amount of money investigating false alarms. Deep learning algorithms have shown they can learn to achieve 99.9% accuracy in certain tasks, where conventional systems would struggle to achieve 95%. In many security use cases, these few percentage points make all the difference.
Not only has deep learning demonstrated its capacity to increase radically the effectiveness of a computer to reliably classify objects and behaviour. It is also making possible the processing and analysis of increasing volumes of video footage in a fraction of the time of earlier analytics. Companies such as Avigilon, Qognify, and IronYun are now marketing analytics that leverage deep learning to turn vast amounts of video footage into usable information in a fraction of the time it would have taken in the past. Video processing software that allows users to interact with their surveillance footage using a Google-like interface and natural language search terms drastically reduces the time it takes to find relevant video footage in an archive that might store video from thousands of feeds.
Facial recognition is an area that has benefited much from deep learning architecture. Indeed, most facial recognition analytics on the market today feature some kind of deep learning. Not only does deep learning increase the accuracy of facial recognition sensors, it also enables faces to be identified in larger and more crowded scenes. In the wake of recent terrorist attacks in crowded locations, this capability could radically change the whole approach to security monitoring, allowing law enforcement to track suspects with far greater speed and efficiency. Herta is one company that specialises in facial recognition in large crowds.
The IHS Markit Video Analytics in Security and Business Intelligence Report – 2017 includes dedicated in-depth research into deep-learning video analytics market, and is available for purchase now
인텔이 독특한 제품을 조용히 등장시켰습니다. 모비디우스(Movidius) 신경망 컴퓨트 스틱(Neural Compute Stick, NCS)이 그것으로 USB 스틱형 코프로세서입니다. 목적은 이름처럼 신경망 연산 등 AI 관련 연산과 AR/VR 관련 연산을 하는 것입니다. 가격은 양심적인 79달러인데 과연 수요가 있을지는 잘 모르겠다는 생각입니다.
사실 사람들이 잘 몰라서 그렇지 신경망 컴퓨트 스틱은 이전에도 있었습니다. 2016년에 나온 Fathom이라는 물건으로 역시 모비디우스만큼이나 생소한 물건입니다. 아무튼 이 모비디우스 컴퓨트 스틱은 Myriad 2 VPU라는 생소한 GPU를 연산용으로 사용하고 있으며 TSCM의 28nm 공정으로 제조되었지만, 1W 당 100GFLOPS의 인공 지능 관련 연산을 수행할 수 있는 높은 전력 대 성능비를 가지고 있습니다.
다만 컴퓨트 스틱 형태로 개발된 점을 봐서도 알 수 있지만, 전력 소모는 2.5W 미만이며 절대 성능 자체가 높은 것은 아니라고 할 수 있습니다. 다만 그래픽 카드를 탑재할 수 없는 경량 노트북에서 인공 지능 및 관련 연산을 수행하는 경우 목적이라면 나름 유용하게 사용할 수 있을지도 모르겠습니다.
모비디우스는 USB 3.0 Type A을 사용하며 텐서플로 대신 Caffe라는 딥 러닝 프레임워크를 지원합니다. 4 GB LPDDR3 메모리를 사용하며 FP16 연산에 특화된 물건이라고 할 수 있습니다. 신기한 재주 가운데 하나는 여러 개의 모비디우스 스틱을 연결해 병렬 연산을 할 수 있다는 점으로 이를 Multi-Stage (stick) Multi-Task Convolutional Neural Network (MTCNN)라고 부릅니다.
일반 사용자는 좀처럼 쓸일이 없는 독특한 물건이지만, AI나 AR/VR이 강조되는 시대 상황에 맞춰 나온 물건이라고 생각합니다. 문제는 지금 인텔이 여기에 매달릴 상황이 아니라는 점이겠죠. 몇 년 사이 조금씩 성능을 향상시킨 CPU만 내놓으면서 인텔은 AMD에 추격을 허용했고 라이젠 출시 이후에는 서버 및 전문가 시장에서 우위를 잃어버릴 위기에 처했습니다.
새로운 시장에 도전하는 것도 좋지만 본래 주력 사업인 PC와 서버 부분을 놓치면 회사가 어려움에 처하게 될 것입니다. 아무래도 지금은 회사의 역량을 새로운 CPU를 개발하는 데 집중해야 할 시기일 것입니다.
[출처] 조용히 등장한 인텔의 인공지능 컴퓨트 스틱 - Movidius Neural Compute Stick|작성자 고든