The movement of people and objects within and around the spaces they occupy, as well as the functions they perform while there, create a complex set of interrelated actions and reactions. Traditional computer vision techniques that use standard optical, thermal, infrared, and other types of cameras are not well suited to capture this complex 3D flow and function information for analysis and decision-making. Cameras capture two-dimensional representations of our three-dimensional environments, which can be easily viewed on flat displays. To analyze the flow and function of a space, extracting depth information from a 2D image can be attempted, but the computing cost is high, and the final representation is a relatively poor approximation.

LiDAR technology is capable of collecting depth information from an environment with precise object locations in a full 3D grid map. This technology overcomes a number of limitations of 2D cameras. LiDAR systems can improve the quality of analysis for data collected during dynamic weather conditions like rain, snow, and fog using AI for improved accuracy. Furthermore, they eliminate the need for ambient lighting, critical for optical cameras to function effectively, and are more robust for lighting environments where glare, reflections, or other harsh conditions are present.

The widespread deployment of high-resolution optical cameras has raised concerns regarding the violation of individual privacy and the potential misuse of the data. Therefore, it is essential to consider a replacement or augmentation of such cameras. LiDAR 3D perception is a promising alternative since it does not contain any biometric data that could be cross-referenced with other sources to identify individuals. This allows operators to do anonymous object tracking while maintaining individuals' privacy.

We are currently evaluating the benefits of integrating LiDAR and other rapidly evolving AI technologies into our Dell Technologies AI solutions for computer vision. We recognize the necessity of adapting swiftly to the ever-changing AI technology landscape in order to aid organizations in monitoring and controlling public and private spaces, enabling them to better comprehend and influence the activities taking place within their managed areas.

There are many technology options available for analyzing historical and real-time data that can be gathered from computer vision solutions. When deciding on the technology and data collection requirements, the focus should be on achieving better organizational outcomes for all stakeholders involved in the use and management of physical environments, such as facilities managers, customers, employees, and constituents.

We have worked with numerous organizations that desire to achieve one or more of the following outcomes while deploying a computer vision solution: enhancing operational efficiency, strengthening safety and security, improving sustainability, enhancing the overall people experience in a physical environment, and generating new revenue opportunities.

In the following sections, we will focus on a few high-value sample use cases that are related to transportation of vehicles and pedestrians. By leveraging LiDAR 3D perception, we will illustrate how a better comprehension of aspects such as vehicle and pedestrian detection, traffic flow optimization, incident detection and response, and data analytics for urban planning can help organizations achieve their desired business outcomes in the context of Intelligent Transportation Systems.