DeepSORT | end pedestrian multi-target tracking

 by   luanshiyinyang Python Version: Current License: No License

kandi X-RAY | DeepSORT Summary

kandi X-RAY | DeepSORT Summary

DeepSORT is a Python library. DeepSORT has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

End-to-end pedestrian multi-target tracking based on DeepSORT algorithm.
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            kandi-support Support

              DeepSORT has a low active ecosystem.
              It has 18 star(s) with 7 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of DeepSORT is current.

            kandi-Quality Quality

              DeepSORT has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              DeepSORT does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              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.
              It has 2927 lines of code, 158 functions and 47 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed DeepSORT and discovered the below as its top functions. This is intended to give you an instant insight into DeepSORT implemented functionality, and help decide if they suit your requirements.
            • Forward a batch of anchors
            • Build the targets
            • Computes the intersection of two boxes
            • Compute the intersection between two boxes
            • Save weights to file
            • Saves a conv model to fp
            • Saves a conv model
            • Runs an image detection using Darknet
            • Print out configuration
            • Perform forward computation
            • Build the targets for the image
            • Stream a video
            • Detects the cv2 boxes using the given weightfile
            • Parse cfg file
            • Split the dataset
            • Update the covariance matrix using the covariance matrix
            • Print out the configuration
            • Calculates the cost of the cost of the detection
            • Parse arguments
            • Draws a training epoch
            • Update the detection with the given kf
            • Recursively reconstruct images in source directory
            • Uploads a single video
            • Run the detector
            • Test the cosine similarity
            • Run darknet detection
            • Train the network
            Get all kandi verified functions for this library.

            DeepSORT Key Features

            No Key Features are available at this moment for DeepSORT.

            DeepSORT Examples and Code Snippets

            No Code Snippets are available at this moment for DeepSORT.

            Community Discussions

            QUESTION

            Unable to find module/folder despite being in the same directory
            Asked 2022-Feb-16 at 00:10

            I am attempting to run a script which calls another python file (copied along with its entire github repo), but I am getting a ModuleNotFoundError: This is despite putting the files into the correct directories.

            How I run it

            1. Activate my python3.9 virtual environment
            2. (newpy39) aevas@aevas-Precision-7560:~/Desktop/Dashboard_2021_12$ python3 dashboard.py where aevas is my username, and ~/Desktop/Dashboard_2021_12 is the folder

            No additional arguments required to run it.

            Imports for dashboard.py

            ...

            ANSWER

            Answered 2022-Feb-16 at 00:10

            The problem is that the models module lives in the /home/aevas/Desktop/Dashboard_2021_12/targetTrack/yolov5 folder, but when you run dashboard.py from /home/aevas/Desktop/Dashboard_2021_12/, the Python interpreter does not look inside the /home/aevas/Desktop/Dashboard_2021_12/targetTrack/yolov5 folder to find modules; it only looks as far as files/folders in /home/aevas/Desktop/Dashboard_2021_12/.

            The usual way to fix this would be by installing yolov5 as a package, but it does not appear that yolov5 contains the code necessary to be installed in such a way. To work around this, you need to let Python know that it needs to look inside the yolov5 folder to find the module.

            The quick-and-dirty way of doing this is to add the folder to the PYTHONPATH environment variable.

            A slightly less quick-and-dirty, but still pretty ugly way of doing it is by dynamically adding the yolov5 folder to sys.path before you try and import the models module. You would do this by adding something like the following to the top of dashboard.py:

            Source https://stackoverflow.com/questions/71107177

            QUESTION

            TypeError: load() missing 1 required positional argument: 'Loader'
            Asked 2021-Nov-23 at 17:20

            I am trying to run this github repo found at this link: https://github.com/HowieMa/DeepSORT_YOLOv5_Pytorch After installing the requirements via pip install -r requirements.txt. I am running this in a python 3.8 virtual environment, on a dji manifold 2g which runs on an Nvidia jetson tx2.

            The following is the terminal output.

            ...

            ANSWER

            Answered 2021-Nov-11 at 05:39

            QUESTION

            How do I get the velocity of a tracked object without calibration?
            Asked 2021-Apr-12 at 14:03

            I am using YoloV4 and Deepsort to detect and track people in a frame.

            My goal is to get speed of a moving person in meaningful units without calibration as I would like to be able to move the camera with this model to different rooms without having to calibrate it each time. I understand this to be a very difficult problem. I am currently getting speed as pixels per second. But that is inaccurate as items closer to frame are "moving" faster.

            My question is if I can use the bounding box of the person detection as a measurement of the size of a person in pixels and if I can average the size of a human being (say 68 inches height by 15 inches width) and have the necessary "calibration" metrics to determine in inches/s the object moved from Point A to Point B in the frame as a reflection of the size of the person from Region A to Region B?

            In short, is there a way to get velocity from the size of an object to determine how fast it is moving in a frame?

            Any suggestions would be helpful! Thanks!

            This is how I am calculating speed now.

            ...

            ANSWER

            Answered 2021-Mar-05 at 20:57

            I think this is the answer I have been looking for.

            I calculate the height and width of the bounding box. I get the pixels per inch of that bounding box by dividing it by the average human height and width. And then I sum the linspace() between the pixels per inch of Region A to the pixels per inch of Region B to get the distance. It's not very accurate though so maybe I can improve on that somehow.

            Mainly the inaccuracies will come from the bounding box. It looks like top to bottom the bounding box is pretty good but left to right (width) it's not good as it's taking into account the arms and legs. I am going to see if I can use just a human head detection as a measurement.

            Source https://stackoverflow.com/questions/66356157

            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

            QUESTION

            how to have full control over a process (start/terminate) which runs in parallel with flask application?
            Asked 2020-May-22 at 22:35

            This is my application architecture:

            In my code there is a pedestrian.py file which uses a while loop to read frames from rtsp link and after doing pedestrian detection process (available in this link), it caches the frame in Redis.

            (please note that in the loop each time the output frame is replaced with the previous output from loop. it means that there exists only one frame in redis in any moment.)

            Then in flask application, I read processed frame from redis and send it for the clients.

            This is the code for my pedestrian detection:

            ...

            ANSWER

            Answered 2020-May-22 at 22:35

            I found a way to automate start/stop the pedestrian detection. more details available in my repo:

            from os.path import join from os import getenv, environ from dotenv import load_dotenv import argparse from threading import Thread

            Source https://stackoverflow.com/questions/61747492

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

            Vulnerabilities

            No vulnerabilities reported

            Install DeepSORT

            You can download it from GitHub.
            You can use DeepSORT like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            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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