Sochor, Petr · Brno University of Technology Digital Library (Brno University of Technology) · 2012
Předmětem této práce je seznámení se s technikami zpracovávání obrazu, pro účel počítačového vidění. Jsou zde použity funkce z knihovny OpenCv zajišťující vyhledávání rohových bodů. Dále jsou zde navržené algoritmy pro vyhledávání shodných bodů na po sobě jdoucích snímcích videosekvence. V práci jsou navržené dvě techniky pro řešení tohoto porovnávání bodů a v závěru práce jsou shrnuté jejich výsledky. Rovněž jsou zde naznačena možná řešení některých nežádoucích vlastností těchto navržených algoritmů.
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Z.M. Hefed · IEEE Potentials · 1999
Object tracking means tracing the progress of objects (or object features) as they move about in a visual scene. It involves processing spatial and temporal changes. Some approaches are discussed together with applications and challenges.
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Francesco Ziliani, Fabrice Moscheni · 2015
Abstract. In the framework of computer vision, the spatio-temporal se-gmentation procedure plays a central role. It aims at identifying in the input image, semantically meaningful features that are relevant for the problem at hand. In this paper, these features are selected to be the objects forming the scene. The objects are dened by their properties of temporal and spatial coherence through the video sequence. They pro-vide a complete partition of the scene into its constituent components. Furthermore, the characteristics of the objects permit to track them thro-ugh time. In this paper, a technique based on a discrete Kalman lter algorithm is proposed to follow the trajectory of the objects. The aim is to obtain a precise prediction of their position and motion. The accurate prediction improves both the recursive spatio-temporal segmentation and object tracking performances, enabling a high level understanding of the scene dynamics. The derived scene representation obtained nds applica-tions in various domains. For instance, it is very well suited for dynamic scene analysis where a deep scene understanding is required. It is also very appealing in the context of second generation video coding.
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Petr Sochor · Digital Repository (National Repository of Grey Literature) · 2012
The goal of this thesis is to get familiar with techniques of image processing for computer vision. Functions from OpenCv library were used for corner point location. Furthermore, algorithms designed for identical point location were also used for location of points in sequence of images represented by a video sequence. In the thesis, two techniques were designed for solving this point comparison. Results of these methods are summarized in the end of thesis. Possible solutions of negative properties of these methods are presented.
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Matthew Bezdek, Jeffrey M. Zacks, Qihong Lu et al. · OSF Preprints (OSF Preprints) · 2024
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Petr Sochor · Digital Repository (National Repository of Grey Literature) · 2012
The goal of this thesis is to get familiar with techniques of image processing for computer vision. Functions from OpenCv library were used for corner point location. Furthermore, algorithms designed for identical point location were also used for location of points in sequence of images represented by a video sequence. In the thesis, two techniques were designed for solving this point comparison. Results of these methods are summarized in the end of thesis. Possible solutions of negative properties of these methods are presented.
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EZHAR, ANIS SYAFIRIN · UTPedia (Universiti Teknologi Petronas) · 2012
„Object tracking‟ is an important task within the field of computer vision. The prevention of theft, the focusing of light to the actor or model automatically during theatre, award show, and concerts, then the law enforcement operation such as search and rescue, prison yard security and helicopter chases. It is needed for simple object tracking designed for simple technique to keep low cost and limited processing power. This project is to develop simple object tracking by using infrared signal. The technique that can be implemented is by allowing the object to be tracked through transmitted infrared. Tracker detects infrared signal and more pointer (spotlight) towards object and the position is the feedback to control system which actuated by means of stepper motor. The adjustment can be in different elements using control system such as P, PI, PD and PID controller. This project successfully can detect the object maximum up to 3.2m.
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Matthew Bezdek, Tan Nguyen, Jeffrey M. Zacks · OSF Preprints (OSF Preprints)
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Qihong Lu, Matthew Bezdek, Tan Nguyen et al. · OSF Preprints (OSF Preprints) · 2024
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Jayanta Mukhopadhyay, Shashaank Aswatha Mattur · 2025
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Linyu Zheng, Ming Tang, Yingying Chen et al. · 2021
Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance. The major reason for this situation is that it is inappropriate and inefficient to apply multiple SOT models directly to the MOT task, although advanced SOT methods are of the strong discriminative power and can run at fast speeds.In this paper, we propose a novel and end-to-end trainable MOT architecture that extends CenterNet by adding an SOT branch for tracking objects in parallel with the existing branch for object detection, allowing the MOT task to benefit from the strong discriminative power of SOT methods in an effective and efficient way. Unlike most existing SOT methods which learn to distinguish the target object from its local backgrounds, the added SOT branch trains a separate SOT model per target online to distinguish the target from its surrounding targets, assigning SOT models the novel discrimination. Moreover, similar to the detection branch, the SOT branch treats objects as points, making its online learning efficient even if multiple targets are processed simultaneously. Without tricks, the proposed tracker achieves MOTAs of 0.710 and 0.686, IDF1s of 0.719 and 0.714, on MOT17 and MOT20 benchmarks, respectively, while running at 16 FPS on MOT17.
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Zixu Zhao, Jiaze Wang, Max Horn et al. · arXiv (Cornell University) · 2023
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines. Unfortunately, they lack two key properties: objects are often split into parts and are not consistently tracked over time. In fact, state-of-the-art models achieve pixel-level accuracy and temporal consistency by relying on supervised object detection with additional ID labels for the association through time. This paper proposes a video object-centric model for MOT. It consists of an index-merge module that adapts the object-centric slots into detection outputs and an object memory module that builds complete object prototypes to handle occlusions. Benefited from object-centric learning, we only require sparse detection labels (0%-6.25%) for object localization and feature binding. Relying on our self-supervised Expectation-Maximization-inspired loss for object association, our approach requires no ID labels. Our experiments significantly narrow the gap between the existing object-centric model and the fully supervised state-of-the-art and outperform several unsupervised trackers.
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Prítel, Pavol · Brno University of Technology Digital Library (Brno University of Technology) · 2014
The thesis deals with detection of moving objects in image sequence. The target of this thesis is a design of object detection algorithm and its application in vehicle counting. The thesis is divided into three parts. The first part of this thesis includes theoretical analysis of given problem. The second part is related to the design and implementation of the algorithm. The third part deals with evaluation of results of the implemented algorithm.
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Calder, Duncan · Clark Digital Commons (Clark University) · 2019
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Ren, J, Kanjo, E, Tieying Lu, K et al. · Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2006
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Christian Merkel · OSF Preprints (OSF Preprints) · 2023
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Kathryn Barker, Roy Allen, Peter McGeorge · Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · 2009
Research has demonstrated that individuals who routinely engage in complex visuospatial tasks (e.g., radar operators) show an enhanced ability to track multiple randomly moving targets. This study examined tracking expertise using members of a University Officer Training Corps (OTCs) who regularly engage in tasks requiring good dynamic spatial cognition. As expected, the results show that OTCs have enhanced tracking ability relative to other undergraduates. More importantly, they support the idea that, while one set of executive processes are involved in the moment-by-moment updating of the visuospatial representations necessary for dynamic, multiple-object tracking, other processes are activated when whole object sets disappear simultaneously, to create a long-term memory trace of the objects' locations at the moment of their disappearance. Expertise only arose in the former processes, but was lost after a short decay period, such as occurred with a delayed response.
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Maheshkumar H. Kolekar · 2018
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al., Zhou et · TIB Data Manager · 2024
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Aiyu Cui · 2018
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