Realtime Object Detection via Deep Learning-based Pipelines

被引：3

作者：

Shanahan, James G. ^{[1
,2
]}

Dai, Liang ^{[3
,4
]}

机构：

[1] Bryant Univ, Smithfield, RI 02917 USA

[2] Univ Calif Berkeley, Berkeley, CA 94720 USA

[3] Facebook, Menlo Pk, CA USA

[4] Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA

来源：

PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) | 2019年

关键词：

Computer vision; image classification; deep learning; object detection; region proposal networks; R-CNN; Fast R-CNN; Faster R-CNN; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); RetinaNet; Mask R-CNN;

D O I：

10.1145/3357384.3360320

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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).

引用

页码：2977 / 2978

页数：2