These challenges can impact the accuracy and efficiency of the system, making it less effective in preventing crime and maintaining security.
As the world becomes increasingly connected, AI-powered video surveillance systems are becoming more prevalent in public spaces, workplaces, and homes. While AI technology has revolutionized video surveillance, it still faces some significant challenges, especially in object detection and tracking.
These challenges can impact the accuracy and efficiency of the system, making it less effective in preventing crime and maintaining security.
In this article, we will delve into the biggest challenges in object detection and tracking for AI video surveillance systems and explore potential solutions to overcome these hurdles. By understanding these challenges, we can work towards improving the capabilities of AI video surveillance and ensuring public safety and security in the digital age.
Lighting conditions
Varying lighting conditions have a significant impact on the performance of AI-based analytics. Different lighting conditions can greatly affect the accuracy of the system, making it less effective in detecting and tracking objects.
“One of the biggest challenges is the varying lighting conditions that can exist in an installation,” explained Rui Barbosa, Product Manager at i-PRO Americas. “Low light can make it difficult for AI-based analytics to perform well. It’s crucial that any AI-based camera be properly spec’d for the environment. A suitable camera should be able to discern color reliably in a low-light environment. Another challenge is with varying light sources, such as fluorescent, LED, or sodium-vapor lights. The different color temperatures can impact the color accuracy and detail that a camera can capture.”
Occlusions or obstructions
Occlusions or obstructions that happen when someone walks behind another person or object are also a challenge for some systems. Properly designed AI-based object tracking that can use color as well as direction can make the difference between losing a person of interest or not.
“Having suitable pixel density or pixels on target can also be a challenge if a camera is not close enough to the object being tracked,” Barbosa said. “Specifying the correct image resolution for the desired coverage area is an important part of good system design. Having suitable processing power at the edge is vital when performing reliable AI-object detection and tracking. We don’t want to burden the network by requiring additional backend servers and infrastructure to process surplus video streams when we can just as easily do everything at the edge.”
False positives
One of the most significant challenges in AI video surveillance is the potential for false positives. False positives occur when the system identifies a threat that is not actually a security issue, leading to unnecessary alerts and distracting security personnel from real security threats.
“While video analytics can provide significant benefits in security environments, there are several challenges that need to be addressed,” said John Rezzonico, CEO of Edge 360. “One of the biggest challenges is the potential for false positives, where the system identifies a threat that is not actually a security issue. This can lead to unnecessary alerts and distract security personnel from real security threats.”
Need for technical expertise
Video analytics systems have become increasingly sophisticated over the years, providing advanced features and capabilities for real-time object detection and tracking. However, due to their complexity, they require significant technical expertise to install, maintain, and operate.
Installing a video analytics system involves a deep understanding of the technology, the environment, and the requirements of the security application. It involves placing the cameras strategically to cover the desired areas, selecting the appropriate lenses, and configuring the settings to optimize the system's performance.
Moreover, maintaining and operating the system requires ongoing technical support, including software upgrades, system troubleshooting, and performance optimization. It also involves analyzing the data generated by the system to detect any anomalies and ensure that the system is functioning optimally. Therefore, it is essential to have experienced and skilled technicians to ensure that the system is functioning optimally and any issues are addressed promptly to minimize downtime and potential security breaches.
Bridging lab-real world gap
According to Victor Hagelbäck, CMO/CPO at Irisity, the biggest challenge in video analytics is to be able to generalize what works in the lab to the real world.
“Rain, fog, and poorly lit scenes will cause problems for even the most on-paper accurate real-time surveillance algorithms,” Hagelbäck said. “What resolves these issues is years of real-world experience in the security industry. Learning from every mistake, the AI made along the way, gradually creating a truly accurate solution.”
Finally, there is a cost component. Having video analytics that does not require an expensive GPU and that enables the use of advanced video surveillance systems for many more customers.
Conclusion
With the rise of artificial intelligence (AI) and the Internet of Things (IoT), video surveillance technology has become an essential component of public safety and security systems. AI-powered video surveillance has become increasingly popular in recent years, enabling real-time object detection and tracking to prevent crime and enhance security in various settings, including public spaces, workplaces, and homes. However, despite its many benefits, AI video surveillance systems still face several challenges, particularly in object detection and tracking.
“Object detection and tracking in video surveillance environments is a challenging task that requires overcoming several challenges, including variations in lighting, motion blur, limited computational resources, and privacy concerns,” explained Greg Skarvelis, Director of Solutions Design and Chief Solutions Architect at Intellicene. “Addressing these challenges requires the development of advanced algorithms, robust sensors, and powerful computing resources. Despite these hurdles, video analytics play a crucial role in ensuring public safety and security and are becoming more proven and advanced in real-world situations.”
Overcoming the challenges associated with object detection and tracking is crucial for the continued advancement and effectiveness of AI video surveillance systems. Advanced algorithms, sensors, and computing resources are being developed to improve the accuracy and efficiency of video analytics in varying environments. With ongoing advancements in AI and video surveillance technology, the potential for public safety and security is vast and promising.