Detecting an AprilTag in real-time demands a combination of efficient software and capable hardware. The computational load largely depends on factors such as the resolution of the input video, the number of tags being tracked, and the complexity of the scene. Real-time detection typically requires a modern processor and may benefit from hardware acceleration, such as using GPUs, especially in environments where multiple tags are present or high-resolution video feeds are processed. For robotics and autonomous systems, where swift and accurate detection is crucial, investing in more robust computing resources ensures that the detection algorithms run smoothly and quickly, thereby supporting timely responses to the tags' data. For developers working on such applications, profiling the software implementation on target hardware during the development phase is critical to ensure that performance requirements are met. This might include testing with various camera inputs under different operating conditions to optimize performance and reliability.