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Digital Video Information Extraction and Object Tracking
Digital Video Information Extraction and Object TrackingНазвание: Digital Video Information Extraction and Object Tracking
Автор: Ronaldo Ferreira, Joaquim Jose de Castro Ferreira, Antonio Jose Ribeiro Neve
Издательство: ITexLi
Год: 2022
Страниц: 187
Язык: английский
Формат: pdf (true)
Размер: 29.9 MB

This book is a result of research done by several researchers and professionals who have highly contributed to the field of image processing.

The research on computer vision systems has been increasing every day and has led to the design of multiple types of these systems with innumerous applications in our daily life. The recent advances in artificial intelligence, together with the huge amount of digital visual data now available, have boosted vision system performance in several ways. Information extraction and visual object tracking are essential tasks in the field of computer vision with a huge number of real-world applications.

The book contains eight chapters divided into three sections. Section 1 consists chapters focusing on the problem of visual tracking. Section 2 includes chapters focusing on information extraction from images. Section 3 presents new advances in image sensors.

The objective of this work is to present an object tracking algorithm developed from the combination of random tree techniques and optical flow adapted in terms of Gaussian curvature. This allows you to define a minimum surface limited by the contour of a two-dimensional image, which must or should not contain a minimum amount of optical flow vector associated with the movement of an object. The random tree will have the purpose of verifying the existence of superfluous vectors of optical flow by discarding them, defining a minimum number of vectors that characterizes the movement of the object. The results obtained were compared with those of the Lucas-Kanade algorithms with and without Gaussian filter, Horn and Schunk and Farneback. The items evaluated were precision and processing time, which made it possible to validate the results, despite the distinct nature between the algorithms.

Visual object tracking (VOT) is one of the fundamental problems and active research areas of computer vision. It is the process of determining the location of an arbitrary object from video sequences. A target with a bounding box is given for the very first frame of the video, and the model predicts the object’s location with height and width in the subsequent frames. VOT has a wide range of vision-based applications, such as intelligent surveillance, autonomous vehicles, game analysis, and human-computer interface. Many researchers have proposed VOT approaches to handle these challenges. Deep features are used more than the handcraft features such as scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG), and local binary patterns (LBP) to solve the tracking problem and perform better against several challenges. Convolutional neural networks (CNN), recurrent neural networks (RNN), autoencoder, residual networks, and generative adversarial networks (GAN) are some popular approaches used to learn deep features for solving vision problems. Among them, CNN is used the most because of its simplistic feed-forward process and better performance on several computer vision applications, such as image classification, object detection, and segmentation. Although CNN has had massive success in solving
vision problems, tracking performance has not improved much because of obtaining
adequate training data for end-to-end training the CNN structure.

1. Object Tracking Using Adapted Optical Flow
2. Siamese-Based Attention Learning Networks for Robust Visual Object Tracking
3. Robust Template Update Strategy for Efficient Visual Object Tracking
4. Cognitive Visual Tracking of Hand Gestures in Real-Time RGB Videos
5. Thresholding Image Techniques for Plant Segmentation
6. A Study on Traditional and CNN Based Computer Vision Sensors for Detection and Recognition of Road Signs with Realization for ADAS
7. Smart-Road: Road Damage Estimation Using a Mobile Device
8. Adsorption-Semiconductor Sensor Based on Nanosized SnO2 for Early Warning of Indoor Fires

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