py-motmetrics
yolov4-deepsort
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py-motmetrics | yolov4-deepsort | |
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1 | 5 | |
1,322 | 1,285 | |
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4.9 | 0.0 | |
1 day ago | 14 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 only |
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py-motmetrics
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HOW to find MOTA and MOTP for MOT evaluation metrics?
I think this repo is a great starting point for your question: https://github.com/cheind/py-motmetrics
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.
What are some alternatives?
multi-object-tracker - Multi-object trackers in Python
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
VolleyVision - Applying Deep Learning Approaches to Volleyball Data
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
Yolo_mark - GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
classy-sort-yolov5 - Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
remote - Moved to https://github.com/labmlai/labml/tree/master/remote
pyenv-installer - This tool is used to install `pyenv` and friends.