yolov4-deepsort
Deep-SORT-YOLOv4
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| yolov4-deepsort | Deep-SORT-YOLOv4 | |
|---|---|---|
| 5 | 1 | |
| 1,287 | 492 | |
| - | - | |
| 0.0 | 0.0 | |
| 20 days ago | about 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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yolov4-deepsort
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Trying to count hikers/day in a video stream
I have a video stream of a hiking trail and am trying to count the number of hikers per day. The camera never moves. At night you can also see headlamps :). Direction not as important as just a count of bodies passing through the image in a given time period. https://drive.google.com/file/d/1BwLzyHhrLafyAj5kYn-AfDVBtBBVQCsO/view. I've tried https://github.com/theAIGuysCode/yolov4-deepsort on the stream but it isn't picking up the people, maybe they're too low res for TF Object Detection?
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How do I train the DeepSORT tracker for a custom class?
I am trying to track objects in a sequence of images in order to count them. I was looking around for robust trackers since in my case, the camera moves with respect to the object. I found the DeepSORT tracker online and it seems like the solution to my problem. However, I am not sure of how I could train it for my own custom classes. I am currently looking at this repository and it seems to almost do the things I want, except for the counting part. Can anyone explain to me how I can train the DeepSORT tracker for my own classes? I am already training a YOLOv4 model on these custom classes. As a result, I have collected a labelled dataset for the training and validation purposes, and if I have to use images for the training.
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Help needed with object tracker implementation
I have tried to implement the YOLOv4 + DeepSort tracking from the AI Guys code presented here, just to get an idea of how to go about this, but from what I understand, I will need to train the DeepSORT tracker for detecting my own classes. I do not know how to do that.
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Will Object Detection models trained on images work on videos too?
You can try a detection and tracking paradigm for videos - it would be a lot more accurate than detection only without the need re-detect every frame which spends a lot of compute. E.g.: https://github.com/theAIGuysCode/yolov4-deepsort
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Installing tensorflow with gpu makes me want to blow my brains out
And totally unecessary!! Next day, I discovered a GitHub project that simply used conda, as below.
Deep-SORT-YOLOv4
What are some alternatives?
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
multi-object-tracker - Multi-object trackers in Python
AI-basketball-analysis - :basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.
remote - Moved to https://github.com/labmlai/labml/tree/master/remote
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
pyenv-installer - This tool is used to install `pyenv` and friends.
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
YOLO-Coco-Dataset-Custom-Classes-Extractor - Get specific classes from the Coco Dataset with annotations for the Yolo Object Detection model for building custom object detection models.