Artificial Intelligence Accelerates Your Multi-Camera Solution

Recent advances in embedded vision have evolved from passive video capturing devices to fully autonomous vision systems. Self-driving cars, drones, or autonomous guided robots require real-time parallel processing, low-latency, and in some cases low current consumption. Multiple camera modules provide surround view and sensor fusion improves the overall vision system, while artificial intelligence and machine learning herald tremendous improvements for recognition and learning tasks in autonomous vision systems.

Building autonomous vision systems grows increasingly challenging, requiring multi-disciplinary expertise in optics, image sensors, computer vision and deep learning. Selecting the right development platform and design methodology is crucial for a successful implementation. Avnet Silica offers embedded vision solutions bundled with tools and reference designs to minimize development time in creating autonomous vision systems.
 

Requirement of a multi-camera solution

• High data rate handling multiple camera input
• Low latency for real-time execution / fast reaction time
• Low power consumption
• Reliability and safety
• Flexibility and reconfigurability
• Intensive computer vision and machine learning
   

Applications

Multi-Camera methodology

The high-resolution multi-camera solution consists of 3 hardware core building blocks: camera modules, a multi-sensor FMC module, and a multi-processor SoC or SOM on a carrier board.

 

Camera modules		Multi-Camera FMC module	Carrier card
onsemi	Maxim Integrated	Maxim Integrated	Xilinx