Vehicle Detection: Explore HOG and SVM Vehicle Recognition

Understand the project

Practical overview

Use it to explore or adapt a notebook-based vehicle-detection approach when you have labeled vehicle and non-vehicle images to work with.

Study how HOG, color histograms and spatial binning can represent vehicle and non-vehicle images.
Explore a linear SVM and sliding-window method for vehicle detection.
Prepare a notebook experiment using labeled car and non-car training images.

Before you choose

The project requires both car and non-car training images; its README suggests more than 1,500 images per class as a good amount.
The documented environment requires Python 3.4 or later and uses a requirements file to install dependencies.
The notebook's specified method is feature-based detection with sliding windows, rather than a documented hosted or deployment service.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the documented Python environment and open the project notebook.

Install the dependencies with the command below.
Documented command
```
pip install -r requirements.txt
```
Launch the notebook interface with jupyter notebook.
Open the project notebook in the launched interface.

The project notebook is available to inspect and work with in Jupyter.

These steps cover environment setup and opening the notebook, not a complete detection run.
Vehicle and non-vehicle training images are needed for the project workflow.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 18 October 2026
Original website title: tatsuyah/vehicle-detection · Official homepage title · retained in its source language