How Deep Learning Benefits The Security Industry

How Deep Learning Benefits The Security Industry

3657
17
SHARE
By : Adler Wu Product Marketing Manager -Vertical Solutions & 3rd Party Integrations, Hikvision Digital Technology Company Ltd.

Data storage devices across the security industry are routinely required to handle an enormous amount and many layers of raw data. As Safe City projects in varying sizes become more prevalent, the number of surveillance nodes has reached the hundreds of thousands. And due to the widespread use of high-definition monitoring, the amount of data involved in security surveillance has increased dramatically in a short time. Efficient collection, analysis, and application of data and the intelligent use of it are becoming ever more critical in this industry. Thus, improving video intelligence appears to be an inevitable, industry-wide goal.

Security users hope that their investment in new products will bring even more benefits beyond simply tracing and tracking persons of interest and evidence collection after a security event. Some examples of added benefits include using the latest technologies to replace the large amount of man-power previously required for searching surveillance footage, detecting anomalous data, and finding ever more efficient ways to allow surveillance to shift from post-incident tracing to alerts during incidents—or even pre-incident alerts. In order to satisfy these demands, new technologies are required. Intelligent video surveillance has been available for many years. However, the outcomes of its application have not been ideal. The emergence of deep learning has enabled these demands to become reality.

The Insufficiency of Traditional Intelligent Algorithms

Traditional intelligent video surveillance has especially strict requirements for a scene’s background. The accuracy of intelligent recognition and analysis in comparable scenarios remains inconsistent. This is primarily due to the fact that traditional intelligent video analysis algorithms still have many flaws.

In an intelligent recognition and analysis process, such as human facial recognition, two key steps are required: First, features are extracted, and second, “classification learning” is performed.

The degree of accuracy in this first step directly determines the accuracy of the algorithm. In fact, most of the system’s calculation and testing workload is consumed in this part. The features in traditional intelligent algorithms are designed by humans and have always been heavily subjective. More abstract features—those that humans have difficulty comprehending or describing—are inevitably missed. With shifting angles and lighting, and especially when the sample size is enormous, many features can be too difficult to detect. Therefore, while traditional intelligent algorithms perform well in very specific environments, subtle changes (image quality, environment, etc.) yield significant challenges to accuracy.

The second step—classification learning—mainly involves target detection and attribute recognition. As the number of available categories for classification rises, so does the difficulty level.  Hence, traditional intelligent analysis technologies are highly accurate in vehicle analysis but not in human and object analysis.  For example, in vehicle detection, a distinction is made between a vehicle and a non-vehicle, so the classification is simple and the level of difficulty is low. To recognize vehicle attributes requires recognition of different vehicle designs, logos, and so on. However, there are relatively few of these, making the classification results generally accurate. On the other hand, if recognition is to be performed on human faces, each person is a classification of its own, and the corresponding categories will be extremely numerous—naturally leading to a very high level of difficulty.

Traditional intelligent algorithms generally use shallow learning models to handle situations with large amounts of data in complex classifications. The analysis results are far from ideal. Furthermore, these results directly restrict the breadth and depth of intelligent applications and further development. Hence the need for increasing the “depth” of intelligence in big data for the security industry is arising.


The Advantages of Deep Learning and its Algorithms

Traditional intelligent algorithms are designed by humans. Whether or not they are designed well depends greatly on experience and even luck, and this process requires a lot of time. So, is it even possible to get machines to automatically learn some of the features? Yes! This is actually the objective of Artificial Intelligence (AI).

The inspiration for deep learning comes from a human brain’s neural networks. Our brains can be seen as a very complex deep learning model. Brain neural networks are comprised of billions of interconnected neurons; deep learning simulates this structure. These multi-layer networks can collect information and perform corresponding actions. They also possess the ability for object abstraction and recreation.

Deep learning is intrinsically different from other algorithms. The way it solves the insufficiencies of traditional algorithms is encompassed in the following aspects.

Deep learning is the next level of AI development. It is beyond machine learning where supervised classification of features and patterns are set into algorithms. Deep learning incorporates unsupervised or “self-learning” principles. Hikvision is developing this concept in its own analytics algorithms. Enhanced accuracy is the result of multi-layer learning and extensive data collection. Application of this algorithm into face recognition, vehicle recognition, human recognition, and other platforms will significantly advance the performance of analytics.

17 COMMENTS

  1. I’m really loving the theme/design of your blog. Do you ever run into any browser compatibility problems? A few of my blog readers have complained about my blog not operating correctly in Explorer but looks great in Chrome. Do you have any advice to help fix this issue?

  2. I don’t even know how I finished up right here, however I believed this publish was once good. I don’t recognise who you might be but definitely you’re going to a well-known blogger in case you aren’t already. Cheers!|

  3. My partner and I stumbled over here by a different website and thought I may as well check things out. I like what I see so now i am following you. Look forward to finding out about your web page repeatedly.|

  4. Hi, I do believe this is an excellent blog. I stumbledupon it 😉 I am going to return once again since I saved as a favorite it. Money and freedom is the greatest way to change, may you be rich and continue to guide other people.|

  5. Greetings I am so glad I found your blog, I really found you by accident, while I was looking on Askjeeve for something else, Regardless I am here now and would just like to say thanks a lot for a incredible post and a all round thrilling blog (I also love the theme/design), I don’t have time to go through it all at the moment but I have saved it and also added your RSS feeds, so when I have time I will be back to read a lot more, Please do keep up the fantastic work.|

  6. Appreciating the dedication you put into your site and detailed information you provide. It’s nice to come across a blog every once in a while that isn’t the same unwanted rehashed material. Wonderful read! I’ve bookmarked your site and I’m adding your RSS feeds to my Google account.|

  7. My spouse and I stumbled over here by a different web page and thought I might as well check things out. I like what I see so now i’m following you. Look forward to checking out your web page yet again.|

  8. Outstanding post however I was wondering if you could write a litte more on this subject? I’d be very grateful if you could elaborate a little bit more. Thanks!|

  9. I’m really impressed with your writing skills and also with the layout on your blog. Is this a paid theme or did you modify it yourself? Either way keep up the nice quality writing, it is rare to see a great blog like this one these days.|

LEAVE A REPLY