Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples.

This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections.

This book serves as a tutorial on data association methods, intended for both students and experts in computer vision.

The book aims to help researchers and practitioners develop techniques and solutions based on the potential of video tracking applications.

But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain.

The book concludes with a chapter on issues critical to the successful implementation of tracking algorithms, such as track initialization and merging"--

This book shows how machine learning can detect moving objects in a digital video stream.

With the increasing need for automated video analysis, visual object tracking became an important task in computer vision.

Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.