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.
No commits in the last 6 months.
Use this if you need robust, real-time object tracking for specific items within a scene using a Raspberry Pi 4, for applications like surveillance, robotics, or interactive displays.
Not ideal if you need to run object tracking on more powerful hardware, require higher frame rates than what a Raspberry Pi 4 can provide, or are not comfortable with C++ development on embedded systems.
Stars
19
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7
Language
C++
License
BSD-3-Clause
Category
Last pushed
Nov 06, 2023
Commits (30d)
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