2024 will not be business as usual. The landscape is rapidly evolving, revealing an interesting future. The security industry is undergoing a remarkable transformation in video technology due to the increasing application of artificial intelligence (AI).
In this article, Rahul Yadav, chief technology officer at Milestone Systems, explores an AI-driven future in which humans watch videos instead of software and humans make decisions. These trends are improving operational performance and opening new opportunities in this field. Join us as we delve into his four AI-driven trends and explore how the symbiotic relationship between AI and human surveillance will redefine his safety and security solutions in 2024. .
Data-driven video technology
The security industry experienced an accelerated impact from AI in 2023, especially through the application of computer vision techniques to video technology in surveillance applications. As a result, the industry trajectory in 2024 will be overwhelmingly focused on data-driven video technologies.
Data-driven video technology uses AI to combine video data with other types of data to extract actionable insights. This is disrupting the security industry, but rather than removing people from the solution, it puts people at the center of the solution. Software is now a tool that identifies objects, recognizes patterns, and generates actionable insights from video data. People act as actors, using their intuition and judgment to validate insights and make informed decisions.
This is driving a strategic shift in video surveillance, evolving it beyond passive observation into a proactive tool for intelligent action. Data-driven video technology includes several AI-driven trends that create new and potentially valuable opportunities both within and outside of security. Let’s take a closer look at these four trends.
Trend 1: Innovative video analysis software
Basic video analytics, such as object detection and counting inside a box, are already widely adopted in safety and security applications. To envision the future of security, we can take inspiration from self-driving cars. These vehicles already leverage advanced video analytics to identify and track objects, and even predict avoidance strategies, all in real time.
Affordable computing power is paving the way for advanced video analytics with detection, tracking, and prediction to enter the security industry. Some of these are still in the development stage, others are currently becoming available, and there are more that are expected to be available for use in applications in the near future.
By extracting contextual information from video data, these advanced technologies can interpret what is happening in a video scene (a sequence of frames) and use this to generate actionable insights for humans. . Here are some techniques that could be game-changers for the security industry.
Segmentation: Enhances understanding of scene dynamics and provides advanced understanding of unfolding events.
Recognition combined with image enhancement: Improves the quality and resolution of video recordings, allowing you to identify objects and actions such as walking, jogging, and running.
Human interaction detection: Recognize and understand the complex ways humans interact with each other and their surroundings.
Anomaly Detection: Enables humans to make informed decisions about highlighted incidents.
Prediction: Looking to the future, rapid advances in large-scale vision and language models (LVMs) have tremendous potential to improve operational performance in the security field. Additionally, the introduction of generative AI provides detailed textual descriptions of objects, their behavior, and interactions, facilitating deeper human understanding. Please keep an eye on this space, where exciting developments will continue!
Incorporating human-involved teams is essential to the successful implementation of these advanced technologies. Future video analytics software will have the ability to detect and alert you to certain behaviors, but ultimately it will be up to the human operator to review the video recording and make informed decisions regarding the necessary actions. This process provides valuable feedback and allows the software to continually improve its functionality with each input. The more feedback the software receives, the smarter it can become at making accurate predictions, ultimately leading to improved performance.
Trend 2: Synthetic data
To accurately interpret video scenes, video analysis software requires large amounts of accurately labeled training data. However, if the data is poorly labeled or limited in scope (for example, if all people are depicted as walking and there are no examples of people in wheelchairs), the data may Bias will occur. Software trained on such biased data will not only inherit the bias and result in less effective solutions, but it will also produce less ethical solutions.
Synthetic data, which are artificially generated rather than sourced from the real world, have great potential in addressing issues of bias. Synthetic data effectively reduces bias by introducing diversity into the training data. It also has the advantage of having accurate labels the first time, eliminating inaccuracies that can result from human error in manual labeling. Additionally, it protects individual privacy and avoids consent-related concerns that arise from using real consumer information without permission or compensation.
Trend 3: Edge AI
In 2024, we will see a significant acceleration in AI development at the edge (AI in devices such as cameras and sensors). Previously, AI tasks were processed in the cloud or in a limited way on local devices, but now there is a middle ground. Thanks to Nvidia and Intel, two important trends have emerged.
First, the edge can now process AI tasks independently, reducing dependence on cloud resources. This allows AI-driven applications to operate closer to the data source, resulting in faster and more efficient processing. There are now many devices at the edge, such as smart cameras and his IoT devices, that can analyze and respond to data in real time.
Second, it is cost-effective to deploy AI at the edge. Reduce dependence on cloud resources, save on bandwidth costs, and reduce latency. This is especially beneficial for security tasks that require real-time monitoring. Edge AI is cost-effective, making it an attractive option for the security industry.
The combination of enhanced capabilities and cost efficiency makes edge AI an attractive security solution of the future. In 2024, we expect further advances in edge AI to enable more advanced applications on devices.
Trend 4: Responsible Technology
Responsible technology is emerging as a prominent trend in 2024 and beyond, as AI drives the shift in video surveillance from observation to action. Future generations are focused on how technology companies approach AI in a responsible manner. For them, innovation is no longer just about who can innovate the fastest. But who is responsible for innovating? Therefore, technology companies must embed responsible technology principles into how they develop, sell, and use their technology.
This trend was highlighted in a 2023 global survey of 150 technology decision makers, which revealed that they intend to weed out potential vendors based on their approach to technology usage. I did. The majority of technology buyers (85%) expect the responsible use of AI, video analytics, and video surveillance to be a prerequisite for engaging with technology vendors in the future.
This research highlights that responsible technology is a key priority for decision makers and an essential business requirement. Within the next three to five years, Responsible Technology will be licensed and operational.
prepare for the future
Data-driven video technologies will continue to shape the security industry roadmap, but the human factor will not be eliminated. Rather, we put people at the center of our solutions. The software currently serves as a peripheral tool to monitor, analyze, and understand video scenes. At the Center, people play a critical role as human participants validating analyzes and making informed decisions.
The human element is critical to future AI-driven intelligence as it relies on high-quality feedback for learning. Human oversight and expertise can help maximize the value of AI-driven security solutions and foster a safer world.
The future of video surveillance, shaped by AI and human expertise, will revolutionize safety and security. Embrace data-driven video technology and the strategic changes it drives, and prepare for his four AI-driven trends coming in 2024.