tatsuyah/vehicle-detection resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1146 stars · 173 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Jupyter Notebook project implementing vehicle detection with classical machine learning and computer vision techniques, built for Udacity's Self-Driving Car Engineer Nanodegree. It uses a linear SVM classifier with HOG features, color histograms, spatial binning, and sliding-window search over dashcam-style images.

Use cases

• detect vehicles in images or video frames
• learn how HOG features and SVMs work for object detection
• complete a self-driving car nanodegree project
• understand sliding window object detection pipelines
• train a car classifier from GTI or KITTI image datasets
• study a worked example of classical computer vision before deep learning

When to choose

• you want an educational walkthrough of classical (non-deep-learning) object detection
• you need a reference implementation of HOG + SVM vehicle detection
• you are working through Udacity's self-driving car curriculum

When to avoid

• you need production-grade or modern deep-learning-based detection (use YOLO or similar)
• you need a maintained library with a license and active support
• you need real-time performance on embedded hardware

Facets

learning-resource · maturity abandoned

machine-learning computer-vision image-processing computer-vision machine-learning autonomous-vehicles education python cross-platform jupyter-notebook svm hog-features sliding-windows vehicle-detection self-driving-car udacity-nanodegree object-detection

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For agents

markdown · JSON · MCP: product_card(name="tatsuyah/vehicle-detection")