yolov4-deepsort | Object tracking implemented with YOLOv4 DeepSort | Computer Vision library

 by   theAIGuysCode Python Version: Current License: GPL-3.0

kandi X-RAY | yolov4-deepsort Summary

kandi X-RAY | yolov4-deepsort Summary

yolov4-deepsort is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow applications. yolov4-deepsort has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has medium support. You can download it from GitHub.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
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            kandi-support Support

              yolov4-deepsort has a medium active ecosystem.
              It has 1181 star(s) with 714 fork(s). There are 15 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 120 open issues and 26 have been closed. On average issues are closed in 43 days. There are 5 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of yolov4-deepsort is current.

            kandi-Quality Quality

              yolov4-deepsort has 0 bugs and 0 code smells.

            kandi-Security Security

              yolov4-deepsort has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              yolov4-deepsort code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              yolov4-deepsort is licensed under the GPL-3.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

            kandi-Reuse Reuse

              yolov4-deepsort releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              yolov4-deepsort saves you 956 person hours of effort in developing the same functionality from scratch.
              It has 2177 lines of code, 116 functions and 21 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed yolov4-deepsort and discovered the below as its top functions. This is intended to give you an instant insight into yolov4-deepsort implemented functionality, and help decide if they suit your requirements.
            • 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
            Get all kandi verified functions for this library.

            yolov4-deepsort Key Features

            No Key Features are available at this moment for yolov4-deepsort.

            yolov4-deepsort Examples and Code Snippets

            Yolov4-deepsort-helmet-detection,prepare environment and run
            Pythondot img1Lines of Code : 2dot img1License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            pip install -r requirements.txt
            
            python detectandtrack.py
              

            Community Discussions

            QUESTION

            Object detection model for detecting rectangular shape (text cursor) in a video?
            Asked 2021-Apr-04 at 17:57

            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 17:57

            This would work if the cursor is the only moving object on the screen. Here is the before and after:

            Before:

            After:

            The code:

            Source https://stackoverflow.com/questions/66929833

            QUESTION

            Problem regarding tensorflow in Jetson nano(ubuntu)
            Asked 2021-Feb-10 at 18:22

            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 18:22

            Just add this line at the starting of the object_tracker.py file and before importing tensorflow:

            Source https://stackoverflow.com/questions/66120992

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install yolov4-deepsort

            To get started, install the proper dependencies either via Anaconda or Pip. I recommend Anaconda route for people using a GPU as it configures CUDA toolkit version for you.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            git@github.com:theAIGuysCode/yolov4-deepsort.git

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