Deploy AI At The Edge But Not On The Camera For More Scale
As the world becomes more connected, the amount of data being generated by devices is increasing at an exponential rate. In particular, the number of cameras being deployed for various applications is growing rapidly. From security cameras to traffic cameras, cameras are being used to monitor and analyze data in real-time. However, the question arises as to where this data should be processed - on the camera or at the edge? In this blog, we will explore why computer vision at the edge is a better choice than AI on a camera.
Processing Speed
The first reason why computer vision at the edge is a better choice is processing speed. AI algorithms require a significant amount of processing power, which may not be available on the camera. Cameras are typically small devices with limited resources, and running AI algorithms on them can be a challenge. On the other hand, the edge can provide more processing power, enabling the AI algorithms to run faster and more efficiently. This is particularly important in applications where real-time processing is critical, such as in security and surveillance.
Bandwidth and Latency
The second reason why computer vision at the edge is a better choice is bandwidth and latency. Cameras can generate a large amount of data, which needs to be transferred to the cloud for analysis. This can result in high bandwidth costs and latency. In contrast, by processing the data at the edge, only the relevant information needs to be transferred to the cloud, reducing the bandwidth requirements and latency. This is particularly important in applications where real-time analysis is needed, such as in smart cities.
Privacy
The third reason why computer vision at the edge is a better choice is privacy. Cameras can be used for monitoring people in public places, such as shopping centers and streets. Processing this data on the camera can raise privacy concerns, as it means that the camera is processing data in real-time. In contrast, by processing the data at the edge, it can be anonymized and aggregated, reducing privacy concerns.
Cost
The fourth reason why computer vision at the edge is a better choice is cost. Cameras with built-in AI capabilities can be expensive, and upgrading existing cameras can also be costly. In contrast, processing the data at the edge can be done with affordable hardware and software, making it a more cost-effective solution.
Scalability
The fifth reason why computer vision at the edge is a better choice is scalability. As the number of cameras increases, it becomes more challenging to process the data on the camera. In contrast, by processing the data at the edge, the processing power can be increased to handle the increased load. This makes it a more scalable solution.
Computer vision at the edge is a better choice than AI on a camera for several reasons. These include processing speed, bandwidth and latency, privacy, cost, and scalability. By processing the data at the edge, it is possible to reduce the bandwidth requirements, increase processing power, reduce privacy concerns, and lower costs. As the number of cameras continues to grow, we can expect to see more organizations turning to computer vision at the edge to analyze the data generated by cameras. The future of computer vision is being shaped by the edge, and it is an exciting time for the industry.
