Vehicle Detection for Self-Driving Cars

Vehicle detection for Self-Driving Cars

The objective of this project is to create a pipeline (model) to draw bounding boxes around cars in a video. Example images and video come from a combination of the GTI vehicle image database and the KITTI vision benchmark suite. We have the following goals:

  • Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
  • Implement a sliding-window technique and use trained classifier to search for vehicles in images
  • Run pipeline on a video stream and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles
  • Estimate a bounding box for vehicles detected

First we perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images.

Here are examples of images from the vehicle class and the non-vehicle class.

Here are some visualized HOG features extracted from the images.

We can implement a sliding-window technique and use our trained classifier to find vehicles in images.

Initially using HOG features and SVM, a heatmap could average the detection results from successive frames. The heatmap was thresholded to a minimum value before labeling regions, to remove false positive. This process was shown above.

With deep learning, however, we can rely on a confidence score to decide the tradeoff between precision and recall. This next figure shows the effect of thresholding SSD detection at different level of confidence.

Eric M. Fischer
PhD Statistics with emphasis in Artificial Intelligence

My research interests are in generative modeling and multi-agent systems.