YoloX-Tracking-ncnn-RPi_64-bit and YoloV5-ncnn-Raspberry-Pi-4
These are ecosystem siblings, representing different versions of a Yolo object detection model implementation (YoloX and YoloV5) optimized for a bare Raspberry Pi 4 using the ncnn inference framework, both provided by the same author.
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 YoloV5-ncnn-Raspberry-Pi-4
Qengineering/YoloV5-ncnn-Raspberry-Pi-4
YoloV5 for a bare Raspberry Pi 4
This project helps embedded systems developers quickly implement object detection on a Raspberry Pi 4. It takes image or video data as input and outputs identified objects with bounding boxes, allowing for real-time analysis on low-power devices. Developers working on edge computing or embedded vision projects would use this.
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