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Beginner-Traffic-Light-Detection-OpenCV-YOLOv3
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8 | 3 | |
3,727 | 34 | |
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3.3 | 3.2 | |
5 months ago | about 3 years ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
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- [D] how does military auto turrets like CWIS not get confused when there are multiple targets?
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Similari 0.26.2: MOT framework with Python bindings
Similari is a Rust/Python framework aimed at building sophisticated tracking systems. With Similari, you can develop highly efficient parallelized SORT, DeepSORT, and other sophisticated multiple-object tracking engines.
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How to improve a YoloV5 model after the first training?
One interesting thing to consider is about using a new model's output as training data is the following. If you use some object tracking algorithm like SORT, you might be able to find frames where the object was "tracked" even though the model didn't detect the object (some type of flickering). In this case, you're able to run the model on video and make it better by giving it these new bounding boxes based on "tracking". Does that make sense?
- Temporal filtering of CNN detectors
- How to track an object between detections with a kalman filter?
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[P] Gait Recognition in the wild
The model is an ST-GCN trained in a completely unsupervised manner, from a LOT of skeleton sequences. Pose estimation was performed with AlphaPose, and tracked using SORT.
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Is it possible to track objects on the go?
Yes it is possible! It's an addition to object detection, so I suggest you look for "object detection tracking". You can find articles like this one which refers to Python libraries like this one.
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I made a traffic light detection program with a self-trained dataset
congratz! that's a great project. since you have the detector operating on videos, you could add a simple tracking-by-detection method like SORT. https://github.com/abewley/sort#using-sort-in-your-own-project it's really easy to use, and creates temporally constistent tracks by associating detections in successive frames.
Beginner-Traffic-Light-Detection-OpenCV-YOLOv3
- Beginner tutorial for Traffic Light Detection using Opencv and YOLOv3
- Traffic Light Detection for Beginners Using OpenCV and YOLOv3
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I made a traffic light detection program with a self-trained dataset
This is my first CV project. I made a Python program that identifies Traffic Lights in video's. The dataset I made consists of hundreds of images of Traffic lights I made myself using my Dashcam. The training was done with a Google Colab GPU.Please take a look at my project and let me know what you think! (https://github.com/initdebugs/Beginner-Traffic-Light-Detection-OpenCV-YOLOv3)
What are some alternatives?
deep_sort - Simple Online Realtime Tracking with a Deep Association Metric
SkunkBooth - Text based command line webcam photobooth app
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
imagezmq - A set of Python classes that transport OpenCV images from one computer to another using PyZMQ messaging.
Yolo_mark - GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
Similari - A framework for building high-performance real-time multiple object trackers
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
opencv-steel-darts - Automatic scoring system for steel darts using OpenCV, a Raspberry Pi 3 Model B and two webcams.
Batch-crop-images - A tool for cropping similar images in a batch with an interface.
imagehub - Receive and save images from multiple Raspberry Pi's
rock_paper_scissors - Rock, paper, scissors - beginner python project