Search Results for "network camera"
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Showing 2 open source projects for "network camera"
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Innago is a free and easy-to-use property management solution. Whether you have 1 unit or 1000, student housing, or commercial properties, Innago is built for you. Our software is designed to save you time and money, so you can spend more time doing the things that matter most.
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CODESOFT is an enterprise-level RFID and barcode label design application offering unmatched flexibility, power, and support. Available in print-only, single-user, and network configurations, CODESOFT is designed to meet all of your barcode labeling needs. Whether your goal is streamlining label printing, printing labels using a form, connecting a scale to your label template, or integrating your business database with your label templates, CODESOFT provides a solution for your organization’s most advanced labeling needs. Connect to SQL or Oracle databases for business system integrations and greater efficiency. CODESOFT includes over 100 barcode symbologies including GS1, supports all common image formats, and enables rich text editing. Native printer drivers are included for over 4,000 thermal, thermal transfer, and inkjet label printers to ensure the best label printing quality.
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and Ubuntu... -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown...
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