VEHICLE DETECTION, TRACKING AND COUNTING

This sample project focuses on "Vechicle Detection, Tracking and Counting" using TensorFlow Object Counting API.


The TensorFlow Object Counting API is used as a base for object counting on this project, more info can be found on this repo.



The developing is on progress! This sample project will be updated soon, the more talented traffic analyzer app will be available in this repo!


General Capabilities of This Sample Project

This sample project has more than just counting vehicles, here are the additional capabilities of it:

ToDos:

The input video can be accessible by this link.

Theory

System Architecture

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.

Tracker

Source video is read frame by frame with OpenCV. Each frames is processed by "SSD with Mobilenet" model is developed on TensorFlow. This is a loop that continue working till reaching end of the video. The main pipeline of the tracker is given at the above Figure.

Model

By default I use an "SSD with Mobilenet" model in this project. You can find more information about SSD in here. See the detection model zoo for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

The minimum vehicle detection threshold can be set in this line in terms of percentage. The default minimum vehicle detecion threshold is 0.5!

Project Demo

Demo video of the project is available on My YouTube Channel.

Installation

Docker setup with Nvidia GPU: Run the demo in the GPU without installing anything, just nvidia-docker. The command to set up this docker:

docker-compose up

Alternative for nvidia-docker, you can follow the installation steps are given below!

1.) Python and pip

Python is automatically installed on Ubuntu. Take a moment to confirm (by issuing a python -V command) that one of the following Python versions is already installed on your system:

The pip or pip3 package manager is usually installed on Ubuntu. Take a moment to confirm (by issuing a pip -V or pip3 -V command) that pip or pip3 is installed. We strongly recommend version 8.1 or higher of pip or pip3. If Version 8.1 or later is not installed, issue the following command, which will either install or upgrade to the latest pip version:

$ sudo apt-get install python3-pip python3-dev # for Python 3.n

2.) OpenCV

See required commands to install OpenCV on Ubuntu in here.

3.) TensorFlow

Install TensorFlow by invoking one of the following commands:

$ pip3 install tensorflow     # Python 3.n; CPU support (no GPU support)
$ pip3 install tensorflow-gpu # Python 3.n; GPU support

4.) TensorFlow Object Detection API

See required commands to install TensorFlow Object Detection API on Ubuntu in here.

If you are still getting problem about installation after completed the installation of the packet that are given above, please check that link out to get detailed info about installation.


Citation

If you use this code for your publications, please cite it as:

@ONLINE{vdtct,
    author = "Ahmet Özlü",
    title  = "Vehicle Detection, Tracking and Counting by TensorFlow",
    year   = "2018",
    url    = "https://github.com/ahmetozlu/vehicle_counting_tensorflow"
}

Author

Ahmet Özlü

License

This system is available under the MIT license. See the LICENSE file for more info.