YoloX-Tracking-ncnn-RPi_64-bit and YoloV6-ncnn-Raspberry-Pi-4
These two tools are competitors, as both implement different versions of the YOLO object detection model (YoloX and YoloV6) specifically optimized with ncnn for deployment on a bare Raspberry Pi 4.
About YoloX-Tracking-ncnn-RPi_64-bit
Qengineering/YoloX-Tracking-ncnn-RPi_64-bit
YoloX with tracking for a bare Raspberry Pi 4 using ncnn.
This project helps you accurately track multiple individual objects moving within a video stream, even when they temporarily block each other. It takes a video file or live camera feed as input and outputs the same video with bounding boxes and unique IDs for each detected object, allowing you to follow their paths over time. It's designed for engineers or hobbyists building custom computer vision applications on a Raspberry Pi 4.
About YoloV6-ncnn-Raspberry-Pi-4
Qengineering/YoloV6-ncnn-Raspberry-Pi-4
YoloV6 for a bare Raspberry Pi using ncnn.
This project helps embedded systems developers deploy object detection capabilities on resource-constrained devices like the Raspberry Pi 4. It takes real-time video feeds or images and outputs bounding boxes around detected objects. This is ideal for developers building IoT solutions, surveillance systems, or robotics applications that need on-device visual processing.
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