The surveillance industry is undergoing a significant transformation, driven by rapid advancements in digitization and automation. This evolution is reshaping the landscape of security operations, introducing innovative technologies, and offering unprecedented capabilities.
Video analytics, a cutting-edge application of the Internet of Things (IoT), is also part of the expanding industry and leverages machine learning algorithms to analyze video feeds. This enables cameras to automatically identify people, objects, and situations. Although these applications are in their infancy, several factors are promoting their expansion. These include the advanced development of analytical algorithms and the reduction in costs for hardware, software, and storage.
Machine learning, particularly deep learning, plays a crucial role in video analytics
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Video Analysis with Deep Learning
Deep learning is a subset of machine learning that uses neural networks to learn patterns. These neural networks are composed of layers of interconnected processing nodes with multiple layers. The rapid advances in deep learning have shown great success in applying AI for video analysis.
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Object Detection in Video Analytics
One of the most common applications of deep learning for video analysis is object detection and tracking. This involves detecting and tracking specific objects in a video sequence. Popular techniques include the use of a convolutional neural network (CNN) to learn complex patterns from data. Real-time Object Detection in video streams has been one of the most important computer vision tasks.
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Use of Algorithms
The most popular algorithms to detect objects in video data include Mask R-CNN, YOLOv3, YOLOR, and YOLOv71. They can be custom-trained with collected video data or pre-trained on large image datasets such as MS COCO1. Such deep learning models are used in software for analyzing video and detecting and tracking objects for trained classes, such as vehicles, people, traffic lights, etc., in real time.
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Advanced Video Analytics
More advanced video analytics software provides functionality for object counting and rule-based analysis, for example, to perform people-counting in areas with large crowds.
By 2025, the economic impact of IoT applications could range from $3.9 trillion to $11.1 trillion annually. The growth of IoT applications could be significantly driven by video analytics applications, which are projected to grow at a compound annual rate of over 50 percent in the next five years.
Recent advancements in video analytics technology have led to significant improvements in several areas
Real-time processing
Modern applications can handle a large volume of video footage in real-time, enabling users to identify potential issues immediately and respond accordingly. For example, store personnel can be alerted to monitor potential shoplifters.
Increased precision
Today’s video analytics applications offer more accurate image analysis. Unlike early surveillance applications that flagged any movement as a potential issue, current applications can distinguish between significant movements and irrelevant ones, such as a falling leaf. Users can also program systems to detect specific visual patterns, like those associated with retail theft or fire alarms.
Enhanced business insights
Advanced image-processing capabilities allow video analytics applications to process multiple visual inputs, some of which may be ambiguous. These applications can analyze the demographics and behaviours of retail customers, providing valuable insights for product assortment and placement, thereby improving store efficiency and customer loyalty.
Access to extensive data and nuanced analyses
Video-analytics software algorithms can now collect and analyze footage from multiple sources, offering more detailed insights. For instance, surveillance applications can identify individuals based on physical characteristics from videos taken at different locations and times.
Innovative use cases
Improved video analytics applications have led to the emergence of new use cases. Some cities are using aggregated data from city and highway cameras to analyze traffic patterns, which could inform future roadway design.
These advancements have demonstrated the value of video analytics in various sectors, from city planning to healthcare. Retailers are utilizing IoT applications with video analytics to assess their customers’ age ranges, demographic profiles, and behaviours. The software then provides recommendations on product assortment and placement.
Here’s a closer look at some of these transformative innovations
The Rise of IP Cameras
Internet Protocol (IP) cameras have become a cornerstone in the surveillance industry. These devices, which transmit digital video over a network, offer several advantages over traditional analog cameras. They provide higher-resolution video, broader coverage areas, and advanced features such as smart motion detection, AI trip wire alarm and analytics.
The adoption of IP cameras has been fueled by their ability to integrate with other security systems and the increasing reliance on cloud-based storage solutions. This integration allows for more efficient monitoring and management of security operations, contributing to the overall effectiveness of security infrastructures.
The Potential of IP Audio
The integration of audio capabilities into IP-based surveillance systems is another noteworthy innovation. IP audio systems can broadcast real-time or pre-recorded messages, enhancing security measures by providing timely alerts or warnings.
These systems can also be used for non-security purposes such as broadcasting background music or informational announcements. The versatility and added value of IP audio make it a promising addition to the surveillance industry.
Digitization Reshaping the Landscape
Digitization is revolutionizing the surveillance industry by enabling the automation of various processes and tasks. Advanced analytics platforms can quickly sift through high volumes of data generated by security tools, identify deviations from the norm, evaluate data from thousands of newly connected assets, and distinguish between legitimate and malicious files, connections, devices, and users.
Moreover, digitization has facilitated the transition from analog surveillance to IP-based systems, leading to improved efficiency and cost-effectiveness.
By 2025, it is projected that cities could reap an economic benefit ranging from $14 billion to $31 billion due to enhanced crime detection and surveillance. Video analytics technology, already a key component in numerous surveillance systems, is poised to take on an even more significant role in the future.
Currently, most established surveillance firms focus on basic video analytics like motion detection, where pricing is the primary competitive factor. On the other hand, start-ups are leading the way in marketing more sophisticated surveillance solutions that boast superior detection capabilities and precision.
AI, Analytics, and Deep Learning Revolutionizing Security Operations
Artificial Intelligence (AI), analytics, and deep learning are playing a pivotal role in modern security operations. These technologies enable machines to analyze vast amounts of data, detect malicious behaviours, and provide enhanced protection against cyberattacks.
Machine learning algorithms can be trained to enable automatic cyber threat detection and generate alerts. Deep learning techniques can help train computers to properly classify inputs, improving their ability to recognize new threats
Leveraging Technology for Business Efficiency
Businesses can leverage these technological advancements to enhance their security infrastructures and overall operations. Implementing technology as a core business strategy helps direct resources toward security upgrades and training as well as disaster planning and recovery.
Advanced security technologies can be used to secure physical assets as well as electronic data, both onsite and remotely. They help analyze large amounts of data and identify patterns that may indicate the presence of a cyber threat.
In 2022, global spending on information technology totalled 169 billion U.S. dollars, with the largest segments being security services, infrastructure protection and network equipment. According to a report by Statista Research Department, about one-fifth (21%) of Canadian businesses reported being impacted by cyber security incidents in 2019. The same year, Canadian businesses reported spending a total of $7 billion directly on measures to prevent, detect and recover from such incidents, with the numbers jumping exponentially between 2022 and 2023.
In conclusion, the transformative power of digitization and automation in the surveillance industry is undeniable. As these technologies continue to evolve and mature, they promise to deliver even more sophisticated capabilities that will further enhance security operations.
