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Superior pedestrian detection with night vision using See3CAM_CU27 USB3 camera & Sony STARVIS

Superior pedestrian detection with night vision using See3CAM_CU27 – a USB3 camera based on Sony STARVIS IMX462

Published on November 10, 2021

The growth of smart cities has made it pertinent for urban planning to be significantly more efficient and proactive. However, a rapid increase in fatal accidents has led to the need for intelligent pedestrian detection – fueled by data collection and analysis. An effective pedestrian detection application involves world-class embedded vision systems with the analytical power to enable accurate people detection on a 24/7 basis. And one of the perquisites of pedestrian detection is the vision system’s ability to capture good quality images in low light or ‘no light’ environments.

e-con Systems’ See3CAM_CU27, a Full HD USB 3.1 Gen 1 camera based on the Sony® STARVIS™ IMX462LQR sensor, is perfect for driving pedestrian detection, smart surveillance, and smart security systems. This camera captures high-quality images in near-infrared regions, and its IR sensitivity unlocks the potential of night vision applications

Through this blog, we look to shed some light on the performance of a pedestrian detection application with an IR source using See3CAM_CU27.

STARVIS Technology for low light conditions

STARVIS is the back-illuminated pixel technology from SONY, especially designed for CMOS image sensors used in surveillance applications. It has a sensitivity of 2000 mV or more per 1 μm2 (color product, when imaging with a 706 cd/m2 light source, F5.6 in 1 s accumulation equivalent). Hence, it ensures high-quality images, in visible-light as well as NIR regions. The Sony® STARVIS™ IMX462LQR sensor also ensures low power consumption and low dark current and avoids smears by adopting RGB color mosaic filters.

Performance of Pedestrian Detection using See3CAM_CU27

We ran the Yolo framework for pedestrian detection in the NVIDIA Jetson AGX Xavier platform to evaluate the performance of the pedestrian detection application, with the See3CAM_CU27 camera connected via USB3 interface. We found out that we could achieve 47 FPS with HD resolution (1280×720). We also tested the See3CAM_CU27 under LUX 0.4 lighting conditions, which are significantly lower than typical dark surroundings.

As you can see in the below image, this camera’s high sensitivity features go a long to facilitate better quantum efficiency, thereby making it effortless to capture clear images in low light conditions.

See3CAM_CU27_LowlightNormal_CAM_Lowlight

Similarly, the below images show the comparison of See3CAM_CU27 with a normal night vision camera in the NIR range (850nm).
See3CAM_CU27_IRNormal_CAM_IR

e-con Systems has developed See3CAM_CU27 exclusively for night vision applications like pedestrian detection and smart surveillance/security that operate in extreme low light conditions.

Visit See3CAM_CU27 to know more about this cutting-edge camera. You can also write to camerasolutions@e-conststems.com if you want more information on how See3CAM_CU27 can accelerate your product development and performance.

Related Cameras:

  1.  See3CAM_CU27– Full HD Ultra Low light USB3 camera based on Sony STARVIS IMX462 sensor with superior NIR sensitivity
  2. e-CAM221_CUMI462_MOD – Sony® Starvis™ IMX462 Ultra-Lowlight Camera Module
  3. e-CAM22_CUNX – Sony® STARVIS™ IMX462 Ultra Low-Light Camera for NVIDIA Jetson Xavier NX
  4. e-CAM23_CUXVR – Sony® STARVIS™ IMX462 Ultra Low-Light Camera for NVIDIA Jetson AGX Xavier

Related Videos

  1. https://www.youtube.com/watch?v=oRSL8ilhRWE
  2. https://www.youtube.com/watch?v=kLWbuVJn-gE
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Vinoth Rajagopalan is an embedded vision expert with 15+ years of experience in product engineering management, R&D, and technical consultations. He has been responsible for many success stories in e-con Systems – from pre-sales and product conceptualization to launch and support. Having started his career as a software engineer, he currently leads a world-class team to handle major product development initiatives

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