Siamese Learning Visual Tracking: A Survey <s> Tracking Definition <s> Preface 1. Introduction to object tracking 2. Filtering theory and non-maneuvering object tracking 3. Maneuvering object tracking 4. Single-object tracking in clutter 5. Single- and multiple-object tracking in clutter: object-existence-based approach 6. Multiple-object tracking in clutter: random-set-based approach 7. Bayesian smoothing algorithms for object tracking 8. Object tracking with time-delayed, out-of-sequence measurements 9. Practical object tracking A. Mathematical and statistical preliminaries B. Finite set statistics (FISST) C. Pseudo-functions in object tracking References Index. <s> BIB001 </s> Siamese Learning Visual Tracking: A Survey <s> Tracking Definition <s> The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements. <s> BIB002 </s> Siamese Learning Visual Tracking: A Survey <s> Tracking Definition <s> Abstract Techniques for estimating cloud motion and stability for intra-hour forecasting using a ground-based sky imaging system are presented. A variational optical flow (VOF) technique was used to determine the sub-pixel accuracy of cloud motion for every pixel. Cloud locations up to 15 min ahead were forecasted by inverse mapping of the cloud map. A month of image data captured by a sky imager at UC San Diego was analyzed to compare the accuracy of VOF forecast with cross-correlation method (CCM) and image persistence method. The VOF forecast with a fixed smoothness parameter was found to be superior to image persistence forecast for all forecast horizons for almost all days and outperform CCM forecast with an average error reduction of 39%, 21%, 19%, and 19% for 0, 5, 10, and 15 min forecasts respectively. Optimum forecasts may be achieved with forecast-horizon-dependent smoothness parameters. In addition, cloud stability and forecast confidence was evaluated by correlating point trajectories with forecast error. Point trajectories were obtained by tracking sub-sampled pixels using optical flow field. Point trajectory length in mintues was shown to increase with decreasing forecast error and provide valuable information for cloud forecast confidence at forecast issue time. <s> BIB003 </s> Siamese Learning Visual Tracking: A Survey <s> Tracking Definition <s> In the human quest for scientific knowledge, empirical evidence is collected by visual perception. Tracking with computer vision takes on the important role to reveal complex patterns of motion that exist in the world we live in. Multi-object tracking algorithms provide new information on how groups and individual group members move through three-dimensional space. They enable us to study in depth the relationships between individuals in moving groups. These may be interactions of pedestrians on a crowded sidewalk, living cells under a microscope, or bats emerging in large numbers from a cave. Being able to track pedestrians is important for urban planning; analysis of cell interactions supports research on biomaterial design; and the study of bat and bird flight can guide the engineering of aircraft. We were inspired by this multitude of applications to consider the crucial component needed to advance a single-object tracking system to a multi-object tracking system—data association. Data association in the most general sense is the process of matching information about newly observed objects with information that was previously observed about them. This information may be about their identities, positions, or trajectories. Algorithms for data association search for matches that optimize certain match criteria and are subject to physical conditions. They can therefore be formulated as solving a "constrained optimization problem"—the problem of optimizing an objective function of some variables in the presence of constraints on these variables. As such, data association methods have a strong mathematical grounding and are valuable general tools for computer vision researchers. This book serves as a tutorial on data association methods, intended for both students and experts in computer vision. We describe the basic research problems, review the current state of the art, and present some recently developed approaches. The book covers multi-object tracking in two and three dimensions. We consider two imaging scenarios involving either single cameras or multiple cameras with overlapping fields of view, and requiring across-time and across-view data association methods. In addition to methods that match new measurements to already established tracks, we describe methods that match trajectory segments, also called tracklets. The book presents a principled application of data association to solve two interesting tasks: first, analyzing the movements of groups of free-flying animals and second, reconstructing the movements of groups of pedestrians. We conclude by discussing exciting directions for future research. <s> BIB004 </s> Siamese Learning Visual Tracking: A Survey <s> Tracking Definition <s> Continuous analysis of single cells, over several cell divisions and for up to weeks at a time, is crucial to deciphering rare, dynamic and heterogeneous cell responses, which would otherwise be missed by population or single-cell snapshot analysis. Although the field of long-term single-cell imaging, tracking and analysis is constantly advancing, several technical challenges continue to hinder wider implementation of this important approach. This is a particular problem for mammalian cells, where in vitro observation usually remains the only possible option for uninterrupted long-term, single-cell observation. Efforts must focus not only on identifying and maintaining culture conditions that support normal cellular behavior while allowing high-resolution imaging over time, but also on developing computational methods that enable semiautomatic analysis of the data. Solutions in microscopy hard- and software, computer vision and specialized theoretical methods for analysis of dynamic single-cell data will enable important discoveries in biology and beyond. <s> BIB005
Tracking BIB001 and in particular visual tracking  , BIB004 is basically a sequential inference problem. As a result, tracking makes predictions of latent variables BIB002 by the way of collecting sufficient evidence with visual sensory inferred from spatiotemporal stimuli in the scene. In most applications, the observations are made by RGB, infrared, depth i.e. LIDAR, ToF cameras, radar or some combination, but other cameras such as event cameras  , hyperspectral cameras and fluorescence microscopy in microbiology BIB005 are becoming increasingly popular. Physical objects or phenomena in the scene cause usually these stimuli, for example natural objects such as humans, animals, cells or artificial objects like cars but also complex phenomena as the formation and movement of clouds BIB003 . The latent variables originate from the mathematical description of scene, objects, camera and their relationships and the assumptions made by the designer. A description is a set of data structures, related by computational feature transforms that give the latent variables the necessary meaning to make inference useful. The image template obtained from the original image is a good example for a well-known simplest description with the four-parameter axis-aligned bounding box as latent variable. In many cases, simple descriptions of appearance, shape, location and motion are sufficient, although, some situations need more complex descriptions such as manoeuvring objects, tracking in clutter BIB001 and tracking multiple objects BIB004 . The latter, for example, needs mutual descriptions in order to infer identity.