yolov4-deepsort | Object tracking implemented with YOLOv4 DeepSort | Computer Vision library
kandi X-RAY | yolov4-deepsort Summary
kandi X-RAY | yolov4-deepsort Summary
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
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Top functions reviewed by kandi - BETA
- Generate detections
- Saves the TrtensorFlow model
- Calculate the cost of the cost function
- Compute the intersection of two bounding boxes
- Convert to TLW
- Calculate NMS based on bounding boxes
- Compute the intersection between two bounding boxes
- Save the trained model
- YolOv3
- Filter boxes with given scores
- YolOv3 test
- Return a yolov instance
- Calculate the distance between detections
- Performs a partial fit
- Update the covariance matrix
- Filter boxes based on score_threshold
- Update the feature with the given kf
- Return the cosine distance between two points
- Parse command line arguments
- Example demo
- N - euclidean distance between points x and y
- Save the TFLite model
- Create image encoder for images
- Load configuration
- Factory function to create a network
- Convert to TLB
- Predict from all tracks
yolov4-deepsort Key Features
yolov4-deepsort Examples and Code Snippets
pip install -r requirements.txt
python detectandtrack.py
Community Discussions
Trending Discussions on yolov4-deepsort
QUESTION
I'm currently doing some research to detect and locate a text-cursor (you know, the blinking rectangle shape that indicates the character position when you type on your computer) from a screen-record video. To do that, I've trained YOLOv4 model with custom object dataset (I took a reference from here) and planning to also implement DeepSORT to track the moving cursor.
Here's the example of training data I used to train YOLOv4:
Here's what I want to achieve:
Do you think using YOLOv4 + DeepSORT is considered overkill for this task? I'm asking because as of now, only 70%-80% of the video frame that contains the text-cursor can be successfully detected by the model. If it is overkill after all, do you know any other method that can be implemented for this task?
Anyway, I'm planning to detect the text-cursor not only from Visual Studio Code window, but also from Browser (e.g., Google Chrome) and Text Processor (e.g., Microsoft Word) as well. Something like this:
I'm considering the Sliding Window method as an alternative, but from what I've read, the method might consume much resources and perform slower. I'm also considering Template Matching from OpenCV (like this), but I don't think it will perform better and faster than the YOLOv4.
The constraint is about the performance speed (i.e, how many frames can be processed given amount of time) and the detection accuracy (i.e, I want to avoid letter 'l' or '1' detected as the text-cursor, since those characters are similar in some font). But higher accuracy with slower FPS is acceptable I think.
I'm currently using Python, Tensorflow, and OpenCV for this. Thank you very much!
...ANSWER
Answered 2021-Apr-04 at 17:57This would work if the cursor is the only moving object on the screen. Here is the before and after:
Before:
After:
The code:
QUESTION
Okay... So I'm trying to run this repo on my jetson nano 2gb...
I followed this official documentation for installation for tensorflow on jetson nano, while I followed https://www.tensorflow.org/install, this official documentation to run tensorflow on windows..
So after setting the dependencies on windows...the started to run smoothly with no problem.
But after setting the dependencies on jetson nano... the code showed this error..
...ANSWER
Answered 2021-Feb-10 at 18:22Just add this line at the starting of the object_tracker.py file and before importing tensorflow:
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