Vehicle-Detection-and-Tracking | Computer vision based vehicle detection | Computer Vision library

by kcg2015 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Vehicle-Detection-and-Tracking Summary

Vehicle-Detection-and-Tracking is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow, OpenCV applications. Vehicle-Detection-and-Tracking has no bugs, it has no vulnerabilities and it has low support. However Vehicle-Detection-and-Tracking build file is not available. You can download it from GitHub.

This repo illustrates the detection and tracking of multiple vehicles using a camera mounted inside a self-driving car. The aim here is to provide developers, researchers, and engineers a simple framework to quickly iterate different detectors and tracking algorithms. In the process, I focus on simplicity and readability of the code. The detection and tracking pipeline is relatively staight forward. It first initializes a detector and a tracker. Next, detector localizes the vehicles in each video frame. The tracker is then updated with the detection results. Finally the tracking results are annotated and displayed in a video frame.

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Vehicle-Detection-and-Tracking has a low active ecosystem.

It has 409 star(s) with 173 fork(s). There are 21 watchers for this library.

It had no major release in the last 6 months.

There are 15 open issues and 19 have been closed. On average issues are closed in 27 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Vehicle-Detection-and-Tracking is current.

Quality

Vehicle-Detection-and-Tracking has 0 bugs and 11 code smells.

Security

Vehicle-Detection-and-Tracking has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Vehicle-Detection-and-Tracking code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Vehicle-Detection-and-Tracking does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Vehicle-Detection-and-Tracking releases are not available. You will need to build from source code and install.

Vehicle-Detection-and-Tracking has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Vehicle-Detection-and-Tracking saves you 166 person hours of effort in developing the same functionality from scratch.

It has 412 lines of code, 19 functions and 5 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Vehicle-Detection-and-Tracking and discovered the below as its top functions. This is intended to give you an instant insight into Vehicle-Detection-and-Tracking implemented functionality, and help decide if they suit your requirements.

Generate pipeline for detection
Assigns detections to a trackers
Draws a label on the given bounding box
Kalman filter
Compute the difference between two boxes
Predict only p
Convert box to pixel coordinates
Get the localized localization
Return the intersection of two boxes
Return the area of two boxes
Find the overlap between two points
Return the union of two boxes
Update the covariance matrix
Loads the image into a numpy array
Apply Kalman filter
Draws a box label
Get the localized location

Get all kandi verified functions for this library.

Vehicle-Detection-and-Tracking Key Features

No Key Features are available at this moment for Vehicle-Detection-and-Tracking.

Vehicle-Detection-and-Tracking Examples and Code Snippets

No Code Snippets are available at this moment for Vehicle-Detection-and-Tracking.

Community Discussions

Trending Discussions on Vehicle-Detection-and-Tracking

Tensorflow - loss not decreasing

QUESTION

Tensorflow - loss not decreasing

Asked 2017-Jul-20 at 15:33

Lately, I have been trying to replicate the results of this post, but using TensorFlow instead of Keras. However, my model loss is not converging as in the code provided. I took care to use the same parameters used by the author, even those not explicitly shown. My complete code can be seen here.

I have already tried different learning rates, optimizers, and batch sizes, but these did not affect the result very much as well.

I found a bunch of other questions related to this problem here in StackOverflow and StackExchange, but most of them had no answer at all. The questions with answers, however, did not help.

I'm using TensorFlow 1.1.0, Python 3.6 and Windows 10.

The most weird thing is that we have the same database and the same model, but just different frameworks. Thus, it was not supposed to give completely different behaviours. Does anyone have suggestions about what should I try to solve this problem, please?

...

ANSWER

Answered 2017-Jul-20 at 15:33

I ran your code basically unmodified, but I looked at the shape of your tf_labels and logits and they're not the same. logits had shape (batch_size,1,1,1) (because you were using a 1x1 convolutional filter) and tf_labels had shape (batch_size,1). I changed your loss line to be

Source https://stackoverflow.com/questions/45191767

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

Vulnerabilities

No vulnerabilities reported

Install Vehicle-Detection-and-Tracking

You can download it from GitHub.
You can use Vehicle-Detection-and-Tracking 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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