How Deep Learning Has Revolutionized Video Analytics

As per the survey done by PWC, the value of commercialized data is expected to grow over $300 billion by the end of 2022. The combination of machine learning with deep learning based on neural networks becomes accurate as more and more data is fed to the systems. There is no doubt that deep learning is better than the traditional methods despite the time and effort needed to once train the systems.

About Deep Neural Networks Training Process

In the mid-2000s, industries went on adopting this technology to train their computers in order to imitate humans. A subset of Artificial Intelligence - deep learning enables technologies to continuously enhance their sophistication and drive other AI applications. Because the systems were expected to work effectively, the Deep neural networks were trained involving the exposure of tagged data.

Large quantities of information are essential for any technology to prove efficiency. Hence Deep neural networks learn to detect, identify and classify data based on the tagging of objects in imagery or video footage. For example, a video analytics system can extract all women in a video scene with the assistance of a deep neural network that is trained to expose large quantities of images of women.

Deep Learning enables AI models to imitate humans. Unlike security services, banks also use rule-based systems to detect fraud. The rule is specified with a set of conditions to trigger a fraud alert.

Banks can understand and train a deep earning model  (that learns from more data given)from past credit card usage. The deployment of AI can continuously learn from thousands and millions of credit card transactions every day. This gives a clear picture of the benefit of automatic learning of new situations based on experience rather than a human writing new rules.

With many use cases of deep learning across every industry, video analytics can also assist medical imaging analysis for clinicians to diagnose any disease. The use of AI methods can help businesses extract insights from massive processed data that IT infrastructure is tasked for.

Increased Computing for Enhancing Deep Neural Network

In yesteryears, Central Processing Units had slow processors that were inefficient to train deep neural networks. As technology advanced new trends such as cloud computing, more data storage, and GPUs were risen to empower DNNs image and video analyzing features.

Besides, increased coverage allowed the system to process more video and aggregate metadata. Over time data became more accurate and actionable and offered deep insights that too cost-effectively.

Modern and intelligent video analytics solutions utilize GPU and Deep Learning for breaking and archiving live video into structured data with rich metadata.

Today, deep learning capabilities can uncover quantifiable data and trends from video metadata to deliver intelligent and actionable business insights along with data-driven safety, security, and operational decision-making opportunities.

The Right Hardware with Right Software

Taking IBM's Power System, for example, the AC922 server is accelerated with four NVIDIA Tesla V100 GPUs for meeting the performance demands of deep learning. This amalgamation of NVIDIA and IBMs superhighway processors is directly connected with the server CPU and GPU together for handling all data movements involved in deep learning. The results displayed the superhighway data transfers up to 5.6 times faster than ever.

The software framework of IBM, known as the Power AI, was designed for enterprise software distribution. The AI and ML-enabled frameworks are curated, tested, and packaged for ease of employment. The adroit and robust framework, fine-tuned with the appropriate hardware displays performance and speedy deployment optimized for deep learning.

The optimized Power AI can assist data scientists to prepare the data better and manage deep learning processes easily. Deep learning uses millions of parameters and creates an extremely complex and highly non-linear internal representation.

Nonlinearity Through Activation

The deep learning model is introduced as activation functions within nonlinearity. The core structure of the deep learning model consists of stacked layers of linear perceptron units with simple matrix multiplications.

The activation function is applied over the output data distributed from a particular layer before moving to another layer.

Reducing Errors

Deep Learning models fall under the class of supervised machine learning methods - extracting the hidden pattern of datasets by observing given examples. The techniques compare the ground truths of predictions and turn the parameters of the model. The difference known between the ground truth and prediction is the classification error.

The above-mentioned parameters of DL contain a set of weights that are connected to neurons across different layers. Further, these weights to specific values reduce the overall classification errors.

This optimization technique to reduce errors sits on the core of deep learning algorithms. Along with nonlinear optimizers, activation functions are another component that resides within the core of deep learning architecture.

Now there is a growing belief that video analytics can move beyond the conventional ways. With semiconductors architecture, enabling faster processing is advancing video analytics solutions. Venture capitalists are spending millions on financing artificial intelligence chip and video analytics software companies.

Three Recommendations for Integrating Video and Analytics

For companies that are planning to expand their use of video analytics, these recommendations can help.

Broader Business Picture

Identify business cases before considering a video analytics solution. You can begin with investing in one business case - a viable strategy to make investments in video, networks, and bandwidth substantial.

Developing your Privacy Policy

Tightening the content's privacy with a robust policy must be addressed as a critical aspect. While business use cases and purposes vary, organizations must develop privacy policies of video content along with the technology they plan on deploying.

Using Automation with Human Checkpoints

The use of automation and machine learning are optimum tools to enhance video and analytics. However, for critical judgments, human experts should be the second point of the review to ensure foolproof and data are accurate.

In a Nutshell -

Supported by Artificial Intelligence and Deep Learning, enhanced Video Analytics Singapore enables object extraction, recognition, classification, and indexing, making video searchable, actionable, and quantifiable.

Empowered video analytics enables operators and systems to review the video in minutes, which once took hours with conventional ways, and identify objects swiftly of interest to extract maximum value with security.