ARTIFICIAL INTELLIGENCE
Artificial intelligence in the surveillance sector By Mats Thulin, Director Core Technologies, Axis Communications If you’re a bit tired of hearing about the potential for artificial intelligence (AI) in our lives and work, you are not alone! AI have been one of the buzzwords of the past few years, and like all buzzwords its overuse and misunderstanding can lead people to be skeptical about its potential. While that’s understandable, we shouldn’t let this prevent us from recognizing some of the real potential of AI in specific applications within video analytics based on machine learning (ML) and deep learning (DL). Defining AI, ML and DL in surveillance Artificial intelligence is a branch of computer science that studies and develops methods that allow computers to simulate intelligent behavior. In general terms AI is a very broad concept, but in the specific context of video analytics the principal focus is to increase operational efficiency and add value by automatically processing and analyzing video streams.
10
In this context a subcategory of AI, machine learning, is more specifically relevant. As its name suggests, machine learning allows computers to improve algorithms through ‘learning’ based on real-world examples. The improved algorithms are then used to analyze images or video sequences to generate alarms, metadata or other information. More recently, attention has turned to a subcategory of ML, deep learning, which describes algorithms based on simulated neural networks. The idea for this type of algorithm was inspired by the human vision system, hence its name – neural networks. In DL networks, layers of operations are arranged in a hierarchy of complex and abstract layers, each layer using information from the previous one to draw its final conclusion. DL models enable more complex analytical algorithms and generally achieve greater precision than traditional ones. In video surveillance systems they
are used primarily in the detection, classification and recognition of different types of objects. However, one drawback of DL algorithms is that they require more computational power and more mathematical operations in comparison to traditional algorithms. Deep learning’s demand for lots of data ML and DL requires relevant huge amounts of input data for training to achieve good quality results. If enough relevant data – and computing power – is available for training, ML- and DL-based methods can efficiently process it to achieve algorithms with higher precision. The computer can analyze thousands of images to find details that characterize specific objects in different scenarios. If the data and their descriptions are of high quality, therefore, an application based on DL is able to achieve even greater accuracy. But availability of highquality data can be a challenge.
