Document Type

Conference Proceeding

Keywords

ccs; computing methodologies; artificial intelligence; computer vision; computer vision problems; object detection

Identifier Data

doi.org/10.1145/3357384.3360320

Publisher

ACM International Conference on Information and Knowledge Management

Abstract

Ever wonder how the Tesla Autopilot system works (or why it fails)? In this tutorial we will look under the hood of self-driving cars and of other applications of computer vision and review state-of-the-art tech pipelines for object detection such as two-stage approaches (e.g., Faster R-CNN) or single-stage approaches (e.g., YOLO/SSD). This is accomplished via a series of Jupyter Notebooks that use Python, OpenCV, Keras, and Tensorflow. No prior knowledge of computer vision is assumed (although it will be help!). To this end we begin this tutorial with a review of computer vision and traditional approaches to object detection such as Histogram of oriented gradients (HOG).

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