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A camera is a hardware device used to capture still images or videos. It is one of the most essential tools used in computer vision. Cameras work by capturing light from a scene and converting it into an electrical signal.
This signal can then be processed using computer vision algorithms to identify objects, detect motion, and measure distances. Cameras come in a variety of shapes, sizes and resolutions to suit different applications. They can be used for a wide range of tasks, such as surveillance, autonomous vehicle navigation, medical imaging, and facial recognition.
The most common type of camera is the charge-coupled device (CCD) camera, which uses an array of photosensitive elements to capture light. The CCD camera can be further classified into analog, digital, and thermal cameras. Digital cameras are usually the most popular choice as they offer greater resolution, more flexibility, and lower costs.
Other types of cameras used in computer vision include time-of-flight (TOF) cameras, which measure the time it takes for light to travel from the camera to the object and back, and depth cameras, which measure the depth of the scene.
Finally, the use of multiple cameras can be used to create a 3D image of the scene, allowing for more accurate object detection and tracking.
In conclusion, cameras are an essential component of computer vision and can be used for a variety of tasks. By choosing the right type of camera for the job at hand, computer vision can be used to capture, analyze, and interpret the world.
The Global Computer Vision Hardware Camera Market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
An innovative computer vision processor is released by indie semiconductor. With the commercial release of iND87540, a highly integrated system-on-chip (SoC) that allows viewing and sensing capabilities at the vehicle’s edge, it has increased the range of automotive camera video processors that it offers.
The iND87540 integrates digital signal processing (DSP), real-time on-chip image signal processing (ISP), and customized hardware to provide viewing and sensing capabilities within the strict power, latency, and compact form factor requirements required for scalable vision architectures.
In order to perform various ADAS features like pedestrian and object identification, blind spot detection, cross-traffic alerts, and driver and occupant monitoring (DMS/OMS), the SoC’s computer vision processing may execute a variety of algorithms. With value-added patented high-performance embedded algorithms like auto calibration (Auto CAL®) and dirty lens detection, indie enhances this class-leading SoC hardware.
Automakers are increasingly looking for camera-based Advanced Driver Assistance System (ADAS) systems that offer volume scalability, across their vehicle classes, in response to demands for higher performance driver and road user safety features from government regulators, new car safety assessors and customers.
This necessitates a “distributed intelligence” architecture approach to vision sensing, high degrees of integration, and low power consumption to satisfy the expectations of mass market deployments.
With these demanding design specifications in mind, independently designed the iND87540. End users also favor iND87540 because it offers real-time image processing for the best detection performance and serves as a pre-processor for strong central compute.
Distributed intelligence is showing promise as a key facilitator for the growth of vision-based ADAS applications as OEMs work to implement vision-based viewing and sensing throughout their model ranges.
The launch of the independent iND87540 aims to leverage this industry trend by providing high-performance vision processing while meeting the expectations of the bulk market on power, cost, and size.
Indie is laying the groundwork for numerous vision-enabled safety and convenience use cases across OEMs’ vehicle classes by combining object detection and real-time video processing onto a single SoC.
