NVIDIA Jetson YOLOv8 Object Tracking

YOLOv8 from Ultralytics

Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks

Regions Counting Using YOLOv8 (Inference on Video)

Region counting is a method employed to tally the objects within a specified area, allowing for more sophisticated analyses when multiple regions are considered. These regions can be adjusted interactively using a Left Mouse Click, and the counting process occurs in real time.
Regions can be adjusted to suit the user's preferences and requirements.

Source Code

https://github.com/ultralytics/ultralytics/tree/main/examples

Install Ultralytics on NVDIA Jetson

https://microcontrollerkits.blogspot.com/2023/09/nvidia-jetson-yolov8-object-detection.html

Run Python Demo Code

Reference

https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/#install-pytorch-and-torchvision

https://wiki.seeedstudio.com/YOLOv8-TRT-Jetson/

https://github.com/ultralytics/ultralytics

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NVIDIA Jetson YOLO Object Detection

Demos showcase how to convert pre-trained yolov3 and yolov4 models through ONNX to TensorRT engines. The code for these 2 demos has gone through some significant changes. More specifically, I have recently updated the implementation with a "yolo_layer" plugin to speed up the inference time of the yolov3/yolov4 models.

What is a YOLO object detector?

When it comes to deep learning-based object detection, there are three primary object detectors you’ll encounter:

R-CNN and their variants, including the original R-CNN, Fast R- CNN, and Faster R-CNN
Single Shot Detector (SSDs)
YOLO

First introduced in 2015 by Redmon et al., their paper, You Only Look Once: Unified, Real-Time Object Detection, details an object detector capable of super real-time object detection, obtaining 45 FPS on a GPU.

You Only Look Once: Unified, Real-Time Object Detection

https://arxiv.org/pdf/1506.02640v3.pdf

YOLOv4

With the original authors work on YOLO coming to a standstill, YOLOv4 was released by Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. The paper was titled YOLOv4: Optimal Speed and Accuracy of Object Detection

Author: Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao
Released: 23 April 2020

YOLOv3

YOLOv3 improved on the YOLOv2 paper and both Joseph Redmon and Ali Farhadi, the original authors, contributed.
Together they published YOLOv3: An Incremental Improvement

The original YOLO papers were are hosted here

Author: Joseph Redmon and Ali Farhadi
Released: 8 Apr 2018

We’ll be using YOLOv3 , YOLOv4 in this blog post, in particular, YOLO trained on the COCO dataset.

The COCO dataset consists of 80 labels.