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Hands-On Computer Vision with TensorFlow 2

You're reading from   Hands-On Computer Vision with TensorFlow 2 Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
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Authors (2):
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Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
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Table of Contents (16) Chapters Close

Preface 1. Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision FREE CHAPTER
2. Computer Vision and Neural Networks 3. TensorFlow Basics and Training a Model 4. Modern Neural Networks 5. Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6. Influential Classification Tools 7. Object Detection Models 8. Enhancing and Segmenting Images 9. Section 3: Advanced Concepts and New Frontiers of Computer Vision
10. Training on Complex and Scarce Datasets 11. Video and Recurrent Neural Networks 12. Optimizing Models and Deploying on Mobile Devices 13. Migrating from TensorFlow 1 to TensorFlow 2 14. Assessments 15. Other Books You May Enjoy

Action recognition

On the other hand, action recognition belongs to the list of tasks that can only be run with a sequence of images. Similar to how we cannot understand a sentence when we are given the words separately and unordered, we cannot recognize an action without studying a continuous sequence of images (refer to Figure 1.6).

Recognizing an action means recognizing a particular motion among a predefined set (for instance, for human actions—dancing, swimming, drawing a square, or drawing a circle). Applications range from surveillance (such as the detection of abnormal or suspicious behavior) to human-machine interactions (such as for gesture-controlled devices):

Figure 1.6: Is Barack Obama in the middle of waving, pointing at someone, swatting a mosquito, or something else?
Only the complete sequence of frames could help to label this action
Since object recognition can be split into object classification, detection, segmentation, and so on, so can action recognition...
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