The objective of this project is to develop a deep learning algorithm so that, together with the use of a stereo camera, it is capable of detecting a person and locating them in the 3D world. The person's location in the x-y plane is obtained from the object detector model, which consists of a convolutional neural network, specifically the U-Net, that outputs heat maps. On the other hand, the person's location in terms of depth (z) is obtained from the depth map given by the ZED stereo camera. The document begins by presenting the techniques used today for object detection (using heat maps). This is followed by an explanation of the key theory behind neural networks; from the simplest neural networks to the convolutional neural networks. To finish with the theoretical part of the project, the hardware and software equipment used is presented. To develop and implement the deep learning algorithm, the first thing that is done is the dataset creation. In order to do that, different images have been selected and prepared to enter the network and train the model (using PyTorch) adapted to the needs of this task. Eight different combination of parameters have been used and eight different models have been obtained. Previously, the metric that will be used to evaluate and compare the different models obtained and choose the one that best suits this application, is defined. Once the final model is chosen, it is stored in the Jetson AGX Xavier and tested using ZED camera images. In this case, the model is verified to being accurate detecting people and the cases where the algorithm fails are identified. The next step of this project consists of applying stereo vision techniques to extract the distance at which the detected person is. A ROS node is created to communicate the ZED camera with the deep learning algorithm. Once the node is ready, it is executed to test the whole program in real time. The ZED color images are passed through the network to detect the person (x, y), and from the ZED depth map, the distance (z) is obtained. From the results obtained, both for the person detection and for the distance extraction, the existing errors in the designed algorithm are identified, and improvements are made by applying filters and code modifications. Thanks to the improvements applied to the results, a sufficient precise algorithm is obtained, capable of detecting a person within a distance range in real time.

OBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems. Audience Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection

One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions

Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. 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. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.

Zusammenfassung: This book offers a comprehensive and unbiased introduction to 3D Computer Vision, ranging from its foundations and essential principles to advanced methodologies and technologies. Divided into 11 chapters, it covers the main workflow of 3D computer vision as follows: camera imaging and calibration models; various modes and means of 3D image acquisition; binocular, trinocular and multi-ocular stereo vision matching techniques; monocular single-image and multi-image scene restoration methods; point cloud data processing and modeling; simultaneous location and mapping; generalized image and scene matching; and understanding spatial-temporal behavior. Each topic is addressed in a uniform manner: the dedicated chapter first covers the essential concepts and basic principles before presenting a selection of typical, specific methods and practical techniques. In turn, it introduces readers to the most important recent developments, especially in the last three years. This approach allows them to quickly familiarize themselves with the subject, implement the techniques discussed, and design or improve their own methods for specific applications. The book can be used as a textbook for graduate courses in computer science, computer engineering, electrical engineering, data science, and related subjects. It also offers a valuable reference guide for researchers and practitioners alike

This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.