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Deep learning technology applications for video surveillance industry

Deep learning technology applications for video surveillance industry

DEEP LEARNING TECHNOLOGY APPLICATIONS FOR VIDEO SURVEILLANCE INDUSTRY

By Paul Sun, President and CEO, IronYun

 

1.      Brief Introduction of Deep Learning

 

The field of artificial intelligence known as machine learning or cognitive computing has in recent years become highly popular. The meteoric rise of deep learning technology over the past several years has been truly dramatic in many industries. The machine learning field has exploded on the scene with the breakthrough in the new ‘Deep learning’ technology.

 

The field of Deep learning evolved from ‘artificial neural nets’ from the 1980s. In the early years of this branch of Artificial Intelligence, the neural nets are modeled after a human’s brain, which consists of over 100 billion neurons. The key limitations of the earlier systems are the difficultly to train the network; and the hardware CPU technologies were too slow to properly train a neural net that can solve meaningful real world applications.

 

The 1980s and 1990s were the dark days of neural network research. Since 2000, the research communities of neural nets have really started to garner leading industrial labs’ attention from the breakthroughs in deep learning work in academia at the University of Toronto, NYU, Stanford and others. Over the past several years, real world applications of deep learning now encompasses many industries including handwriting recognition, language translation, automatic game (chess/go) playing, object classification, face recognition, medical image analysis, autonomous driving cars and many other fields.

 

One example of the excitement with Deep learning technology is the recent breakthrough from Google’s AlphaGo, a computer program that for the first time beat a professional human Go player in October, 2015. The sophistication of the Deep learning based program has astonished many in the field of artificial intelligence due the complexity of the ancient Asia GO game, which is considerably more complicated than chess.

 

2.    Video surveillance applications with deep learning

 

Although deep learning has been applied to many industries with breakthrough results comparing to legacy systems, not all applications are suitable using deep learning. In the field of video surveillance, several applications stands out that can benefit from deep learning.

 

a.    Face recognition

Deep learning technology has significantly improved the accuracy rate of face recognition. Most of today’s top performing commercial face recognition products are based on deep learning. The accuracy has reached 99.9% for controlled environment like airport immigration face recognition applications.

 

b.    Persons and Object detection

Person detection and object detection is another area where deep learning has shown tremendous progress. For example, over the past five years, IMAGENET [3] has organized the large scale visual recognition challenge, where image software algorithms are challenged to detect, classify and localize a database of over 150,000 photographs collected from flickr and other search engines. The dataset are labeled into 1,000 object categories.

 

 

3.    New applications and features of deep learning based video surveillance solutions

 

A key advantage of deep learning based algorithms over legacy computer vision algorithms is that deep learning system can be continuously trained and improved with better and more datasets. Many applications have shown that deep learning systems can “learn” to achieve 99.9% accuracy for certain tasks. In rigid computer algorithms, it is very difficult to improve a system past 95% accuracy.

The second advantage with deep learning system is the “abnormal” event detection. Deep learning system has shown remarkable ability to detect undefined or unexpected events. This feature has the true potential of significantly reduce false positive detection events that plagues many security video analytics systems. In fact, the inability to reduce false positive detection rate is the key problem in video surveillance industry; and has to-date prevented the wide scale acceptance of many vendor’s intelligent video analytics solutions.

 

4.    Conclusion

 

Similar to cloud computing and big data technologies, deep learning technology is now emerging as the third wave of rapid advances that have taken over the information industry by storm.

 

Over the next decade, almost all areas in the technology sector will be touched by the advances of cloud, big data and machine learning. For the video surveillance industry, this is welcome news, for the industry has been lacking in innovations that can significantly advance the state-of-the art.

 

Quotes: Deep learning technology is now emerging as the third wave of rapid advances that have taken over the information industry by storm

 

Sophistication of the Deep learning based program has astonished many in the field of artificial intelligence due the complexity of the ancient Asia GO game