Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Object Tracking PDF full book. Access full book title Object Tracking.
| Author | : |
| Publisher | : Cambridge University Press |
| Total Pages | : 389 |
| Release | : 2011-07-28 |
| Genre | : Mathematics |
| ISBN | : 0521876281 |
Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples.
| Author | : Ashish Kumar |
| Publisher | : Springer Nature |
| Total Pages | : 280 |
| Release | : 2023-10-27 |
| Genre | : Computers |
| ISBN | : 9819932882 |
With the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. 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 various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking.· Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions.
| Author | : Weiwei Xing |
| Publisher | : Springer Nature |
| Total Pages | : 202 |
| Release | : 2021-11-18 |
| Genre | : Computers |
| ISBN | : 9811662428 |
The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.
| Author | : Vivian Siahaan |
| Publisher | : BALIGE PUBLISHING |
| Total Pages | : 204 |
| Release | : 2024-04-17 |
| Genre | : Computers |
| ISBN | : |
The first project, gui_motion_analysis_gbbm.py, is designed to streamline motion analysis in videos using the Gradient-Based Block Matching Algorithm (GBBM) alongside a user-friendly Graphical User Interface (GUI). It encompasses various objectives, including intuitive GUI design with Tkinter, enabling video playback control, performing optical flow analysis, and allowing parameter configuration for tailored motion analysis. The GUI also facilitates interactive zooming, frame-wise analysis, and offers visual feedback through motion vector overlays. Robust error handling and multi-instance support enhance stability and usability, while dynamic title updates provide context within the interface. Overall, the project empowers users with a versatile tool for comprehensive motion analysis in videos. By integrating the GBBM algorithm with an intuitive GUI, gui_motion_analysis_gbbm.py simplifies motion analysis in videos. Its objectives range from GUI design to parameter configuration, enabling users to control video playback, perform optical flow analysis, and visualize motion patterns effectively. With features like interactive zooming, frame-wise analysis, and visual feedback, users can delve into motion dynamics seamlessly. Robust error handling ensures stability, while multi-instance support allows for concurrent analysis. Dynamic title updates enhance user awareness, culminating in a versatile tool for in-depth motion analysis. The second project, gui_motion_analysis_gbbm_pyramid.py, is dedicated to offering an accessible interface for video motion analysis, employing the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach. Its objectives encompass several crucial aspects. Primarily, the project responds to the demand for motion analysis in video processing across diverse domains like computer vision and robotics. By integrating the GBBM algorithm into a GUI, it democratizes motion analysis, catering to users without specialized programming or computer vision skills. Leveraging the GBBM algorithm's effectiveness, particularly with the Pyramid Approach, enhances performance and robustness, enabling accurate motion estimation across various scales. The GUI offers extensive control options and visualization features, empowering users to customize analysis parameters and inspect motion dynamics comprehensively. Overall, this project endeavors to advance video processing and analysis by providing an intuitive interface backed by cutting-edge algorithms, fostering accessibility and efficiency in motion analysis tasks. The third project, gui_motion_analysis_gbbm_adaptive.py, introduces a GUI application for video motion estimation, employing the Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size. Users can interact with video files, control playback, navigate frames, and visualize optical flow between consecutive frames, facilitated by features like zooming and panning. Developed with Tkinter in Python, the GUI provides intuitive controls for adjusting motion estimation parameters and playback options upon launch. At its core, the application dynamically adjusts block sizes based on local gradient magnitude, enhancing motion estimation accuracy, especially in areas with varying complexity. Utilizing PIL and OpenCV libraries, it handles image processing tasks and video file operations, enabling users to interact with the video display canvas for enhanced analysis. Overall, gui_motion_analysis_gbbm_adaptive.py offers a versatile solution for motion analysis in videos, empowering users with visualization tools and parameter customization for diverse applications like video compression and object tracking. The fourth project, gui_motion_analysis_gbbm_lucas_kanade.py, introduces a GUI for motion estimation in videos, incorporating both the Gradient-Based Block Matching Algorithm (GBBM) and Lucas-Kanade Optical Flow. It begins by importing necessary libraries such as tkinter for GUI development, PIL for image processing, imageio for video file handling, cv2 for computer vision operations, and numpy for numerical computation. The VideoGBBM_LK_OpticalFlow class serves as the application container, initializing attributes and defining methods for video loading, playback control, parameter setting, frame display, and optical flow visualization. With features like zooming, panning, and event handling for user interactions, the script offers a comprehensive tool for visualizing and analyzing motion dynamics in videos using two distinct optical flow estimation techniques. The fifth project, gui_motion_analysis_gbbm_sift.py, introduces a GUI application for optical flow analysis in videos, employing both the Gradient-Based Block Matching Algorithm (GBBM) and Scale-Invariant Feature Transform (SIFT). It begins by importing essential libraries such as tkinter for GUI development, PIL for image processing, imageio for video handling, and OpenCV for computer vision tasks like optical flow computation. The VideoGBBM_SIFT_OpticalFlow class orchestrates the application, initializing GUI elements and defining methods for video loading, playback control, frame display, and optical flow computation using both GBBM and SIFT algorithms. With features for parameter adjustment, frame navigation, zooming, and event handling for user interactions, the script offers a user-friendly interface for in-depth optical flow analysis, enabling insights into motion patterns and dynamics within videos. The sixth project, gui_motion_analysis_gbbm_orb.py script, offers a user-friendly interface for motion estimation in videos, utilizing both the Gradient-Based Block Matching Algorithm (GBBM) and ORB (Oriented FAST and Rotated BRIEF) optical flow techniques. Its primary goal is to enable users to analyze and visualize motion dynamics within video files effortlessly. The GUI application provides functionalities for opening video files, navigating frames, adjusting parameters like zoom scale and step size, and controlling playback with buttons for play, pause, stop, next frame, and previous frame. Key to the application's functionality is its ability to compute and visualize optical flow using both GBBM and ORB algorithms. Optical flow, depicting object motion in videos, is represented with vectors overlaid on video frames, aiding users in understanding motion patterns and dynamics. Interactive features such as mouse wheel zooming and dragging enhance user exploration of video frames and optical flow visualizations, allowing dynamic adjustment of viewing perspective to focus on specific regions or analyze motion at different scales. Overall, this project provides a comprehensive tool for video motion analysis, merging user-friendly interface elements with advanced motion estimation techniques to empower users in tasks ranging from surveillance to computer vision research. The seventh project showcases object tracking using the Gradient-Based Block Matching Algorithm (GBBM), vital in various computer vision applications like surveillance and robotics. By continuously locating and tracking objects of interest in video streams, it highlights GBBM's practical application for real-time tracking. The GUI interface simplifies interaction with video files, allowing easy opening and visualization of frames. Users control playback, navigate frames, and adjust zoom scale, while the heart of the project lies in GBBM's implementation for tracking objects. GBBM estimates object motion by comparing pixel blocks between consecutive frames, generating motion vectors that describe the object's movement. Users can select regions of interest for tracking, adjust algorithm parameters, and receive visual feedback through dynamically adjusting bounding boxes around tracked objects, making it an educational tool for experimenting with object tracking techniques within an accessible interface. The eight project endeavors to create an application for object tracking using the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach, catering to various computer vision applications like surveillance and autonomous vehicles. Built with Tkinter in Python, the user-friendly interface presents controls for video display, object tracking, and parameter adjustment upon launch. Users can load video files, play, pause, navigate frames, and adjust zoom levels effortlessly. Central to the application is the GBBM algorithm with a pyramid approach for robust object tracking. By refining search spaces at multiple resolutions, it efficiently estimates motion vectors, accommodating scale variations and occlusions. The application visualizes tracked objects with bounding boxes on the video canvas and updates object coordinates dynamically, providing users with insights into object movement. Advanced features, including dynamic parameter adjustment, enhance the algorithm's adaptability, enabling users to fine-tune tracking based on video characteristics and requirements. Overall, this project offers a practical implementation of object tracking within an accessible interface, catering to users across expertise levels in computer vision. The ninth project, "Object Tracking with Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size", focuses on developing a graphical user interface (GUI) application for object tracking in video files using computer vision techniques. Leveraging the GBBM algorithm, a prominent method for motion estimation, the project aims to enable efficient object tracking across video frames, enhancing user interaction and real-time monitoring capabilities. The GUI interface facilitates seamless video file loading, playback control, frame navigation, and real-time object tracking, empowering users to interact with video frames, adjust zoom levels, and monitor tracked object coordinates throughout the video sequence. Central to the project's functionality is the adaptive block size variant of the GBBM algorithm, dynamically adjusting block sizes based on gradient magnitudes to improve tracking accuracy and robustness across various scenarios. By simplifying object tracking processes through intuitive GUI interactions, the project caters to users with limited programming expertise, fostering learning opportunities in computer vision and video processing. Additionally, the project serves as a platform for collaboration and experimentation, promoting knowledge sharing and innovation within the computer vision community while showcasing the practical applications of computer vision algorithms in surveillance, video analysis, and human-computer interaction domains. The tenth project, "Object Tracking with SIFT Algorithm", introduces a GUI application developed with Python's tkinter library for tracking objects in videos using the Scale-Invariant Feature Transform (SIFT) algorithm. Upon launching, users access a window featuring video display, center coordinates of tracked objects, and control buttons. Supported video formats include mp4, avi, mkv, and wmv, with the "Open Video" button enabling file selection for display within the canvas widget. Playback control buttons like "Play/Pause," "Stop," "Previous Frame," and "Next Frame" facilitate seamless navigation and video playback adjustments. A zoom combobox enhances user experience by allowing flexible zoom scaling. The SIFT algorithm facilitates object tracking by detecting and matching keypoints between frames, estimating motion vectors used to update the bounding box coordinates of the tracked object in real-time. Users can manually define object bounding boxes by clicking and dragging on the video canvas, offering both automated and manual tracking options for enhanced user control. The eleventh project, "Object Tracking with ORB (Oriented FAST and Rotated BRIEF)", aims to develop a user-friendly GUI application for object tracking in videos using the ORB algorithm. Utilizing Python's Tkinter library, the project provides an interface where users can open video files of various formats and interact with playback and tracking functionalities. Users can control video playback, adjust zoom levels for detailed examination, and utilize the ORB algorithm for object detection and tracking. The application integrates ORB for computing keypoints and descriptors across video frames, facilitating the estimation of motion vectors for object tracking. Real-time visualization of tracking progress through overlaid bounding boxes enhances user understanding, while interactive features like selecting regions of interest and monitoring bounding box coordinates provide further control and feedback. Overall, the "Object Tracking with ORB" project offers a comprehensive solution for video analysis tasks, combining intuitive controls, real-time visualization, and efficient tracking capabilities with the ORB algorithm.
| Author | : Pier Luigi Mazzeo |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 208 |
| Release | : 2019-12-18 |
| Genre | : Computers |
| ISBN | : 1789851572 |
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.
| Author | : Roy Streit |
| Publisher | : Springer Nature |
| Total Pages | : 221 |
| Release | : 2020-11-26 |
| Genre | : Technology & Engineering |
| ISBN | : 3030611914 |
The book shows that the analytic combinatorics (AC) method encodes the combinatorial problems of multiple object tracking—without information loss—into the derivatives of a generating function (GF). The book lays out an easy-to-follow path from theory to practice and includes salient AC application examples. Since GFs are not widely utilized amongst the tracking community, the book takes the reader from the basics of the subject to applications of theory starting from the simplest problem of single object tracking, and advancing chapter by chapter to more challenging multi-object tracking problems. Many established tracking filters (e.g., Bayes-Markov, PDA, JPDA, IPDA, JIPDA, CPHD, PHD, multi-Bernoulli, MBM, LMBM, and MHT) are derived in this manner with simplicity, economy, and considerable clarity. The AC method gives significant and fresh insights into the modeling assumptions of these filters and, thereby, also shows the potential utility of various approximation methods that are well established techniques in applied mathematics and physics, but are new to tracking. These unexplored possibilities are reviewed in the final chapter of the book.
| Author | : Ashish Kumar |
| Publisher | : CRC Press |
| Total Pages | : 248 |
| Release | : 2023-11-10 |
| Genre | : Technology & Engineering |
| ISBN | : 1000991008 |
This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods, various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also: Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methods. Elaborates on the salient features of deep learning trackers along with traditional trackers, wherein the handcrafted features are fused to reduce computational complexity. Illustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenarios. Explores the future research directions for visual tracking by analyzing the real-time applications. The book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques, evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.
| Author | : Grinberg, Michael |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 296 |
| Release | : 2018-08-10 |
| Genre | : Electronic computers. Computer science |
| ISBN | : 3731507811 |
This work proposes a feature-based probabilistic data association and tracking approach (FBPDATA) for multi-object tracking. FBPDATA is based on re-identification and tracking of individual video image points (feature points) and aims at solving the problems of partial, split (fragmented), bloated or missed detections, which are due to sensory or algorithmic restrictions, limited field of view of the sensors, as well as occlusion situations.
| Author | : Vivian Siahaan |
| Publisher | : BALIGE PUBLISHING |
| Total Pages | : 174 |
| Release | : 2024-04-26 |
| Genre | : Computers |
| ISBN | : |
The first project, BoostingTracker.py, is a Python application that leverages the Tkinter library for creating a graphical user interface (GUI) to track objects in video sequences. By utilizing OpenCV for the underlying video processing and object tracking mechanics, alongside imageio for handling video files, PIL for image displays, and matplotlib for visualization tasks, the script facilitates robust tracking capabilities. At the heart of the application is the BoostingTracker class, which orchestrates the GUI setup, video loading, and management of tracking states like playing, pausing, or stopping the video, along with enabling frame-by-frame navigation and zoom functionalities. Upon launching, the application allows users to load a video through a dialog interface, select an object to track by drawing a bounding box, and then observe the tracker in action as it follows the object across frames. Users can interact with the video playback through intuitive controls for adjusting the zoom level and applying various image filters such as Gaussian blur or wavelet transforms to enhance video clarity and tracking accuracy. Additional features include the display of object center coordinates in real-time and the capability to analyze color histograms of the tracked areas, providing insights into color distribution and intensity for more detailed image analysis. The BoostingTracker.py combines these features into a comprehensive package that supports extensive customization and robust error handling, making it a valuable tool for applications ranging from surveillance to multimedia content analysis. The second project, MedianFlowTracker, utilizes the Python Tkinter GUI library to provide a robust platform for video-based object tracking using the MedianFlow algorithm, renowned for its effectiveness in tracking small and slow-moving objects. The application facilitates user interaction through a feature-rich interface where users can load videos, select objects within frames via mouse inputs, and use playback controls such as play, pause, and stop. Users can also navigate through video frames and utilize a zoom feature for detailed inspections of specific areas, enhancing the usability and accessibility of video analysis. Beyond basic tracking, the MedianFlowTracker offers advanced customization options allowing adjustments to tracking parameters like window size and the number of grid points, catering to diverse tracking needs across different video types. The application also includes a variety of image processing filters such as Gaussian blur, median filtering, and more sophisticated methods like anisotropic diffusion and wavelet transforms, which users can apply to video frames to either improve tracking outcomes or explore image processing techniques. These features, combined with the potential for easy integration of new algorithms and enhancements due to its modular design, make the MedianFlowTracker a valuable tool for educational, research, and practical applications in digital image processing and video analysis. The third project, MILTracker, leverages Python's Tkinter GUI library to provide a sophisticated tool for tracking objects in video sequences using the Multiple Instance Learning (MIL) tracking algorithm. This application excels in environments where the training instances might be ambiguously labeled, treating groups of pixels as "bags" to effectively handle occlusions and visual complexities in videos. Users can dynamically interact with the video, initializing tracking by selecting objects with a bounding box and adjusting tracking parameters in real-time to suit various scenarios. The application interface is intuitive, offering functionalities like video playback control, zoom adjustments, frame navigation, and the application of various image processing filters to improve tracking accuracy. It supports extensive customization through an adjustable control panel that allows modification of tracking windows, grid points, and other algorithm-specific parameters. Additionally, the MILTracker logs the movement trajectory of tracked objects, providing valuable data for analysis and further refinement of the tracking process. Designed for extensibility, the architecture facilitates the integration of new tracking methods and enhancements, making it a versatile tool for applications ranging from surveillance to sports analysis. The fourth project, MOSSETracker, is a GUI application crafted with Python's Tkinter library, utilizing the MOSSE (Minimum Output Sum of Squared Error) tracking algorithm to enhance real-time object tracking within video sequences. Aimed at users with interests in computer vision, the application combines essential video playback functionalities with powerful object tracking capabilities through the integration of OpenCV. This setup provides an accessible platform for those looking to delve into the dynamics of video processing and tracking technologies. Structured for ease of use, the application presents a straightforward interface that includes video controls, zoom adjustments, and display of tracked object coordinates. Users can initiate tracking by selecting an object within the video through a draggable bounding box, which the MOSSE algorithm uses to maintain tracking across frames. Additionally, the application offers a suite of image processing filters like Gaussian blur and wavelet transformations to enhance tracking accuracy or demonstrate processing techniques. Overall, MOSSETracker not only facilitates effective object tracking but also serves as an educational tool, allowing users to experiment with and learn about advanced video analysis and tracking methods within a practical, user-friendly environment. The fifth project, KCFTracker, is utilizing Kernelized Correlation Filters (KCF) for object tracking, is a comprehensive application built using Python. It incorporates several libraries such as Tkinter for GUI development, OpenCV for robust image processing, and ImageIO for video stream handling. This application offers an intuitive GUI that allows users to upload videos, manually draw bounding boxes to identify areas of interest, and adjust tracking parameters in real-time to optimize performance. Key features include the ability to apply a variety of image filters to enhance video quality and tracking accuracy under varying conditions, and advanced functionalities like real-time tracking updates and histogram analysis for in-depth examination of color distributions within the video frame. This melding of interactive elements, real-time processing capabilities, and analytical tools establishes the MILTracker as a versatile and educational platform for those delving into computer vision. The sixth project, CSRT (Channel and Spatial Reliability Tracker), features a high-performance tracking algorithm encapsulated in a Python application that integrates OpenCV and the Tkinter graphical user interface, making it a versatile tool for precise object tracking in various applications like surveillance and autonomous vehicle navigation. The application offers a user-friendly interface that includes video playback, interactive controls for real-time parameter adjustments, and manual bounding box adjustments to initiate and guide the tracking process. The CSRT tracker is adept at handling variations in object appearance, lighting, and occlusions due to its utilization of both channel reliability and spatial information, enhancing its effectiveness across challenging scenarios. The application not only facilitates robust tracking but also provides tools for video frame preprocessing, such as Gaussian blur and adaptive thresholding, which are essential for optimizing tracking accuracy. Additional features like zoom controls, frame navigation, and advanced analytical tools, including histogram analysis and wavelet transformations, further enrich the user experience and provide deep insights into the video content being analyzed.
| Author | : |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 212 |
| Release | : 2022-08-17 |
| Genre | : Computers |
| ISBN | : 1839694602 |
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.This book is a result of research done by several researchers and professionals who have highly contributed to the field of image processing. It contains eight chapters divided into three sections. Section 1 consists of four chapters focusing on the problem of visual tracking. Section 2 includes three chapters focusing on information extraction from images. Finally, Section 3 includes one chapter that presents new advances in image sensors.
