Academic literature on the topic 'Sledovací algoritmy'

SHRNUTÍ Članek se zabyva zhodnocenim vyvoje projektu SMALL na ČVUT v Praze, ktery se tykal zkoušek systemů vedouci k autonomnimu navaděni vozidel. Pomoci zmenšeneho modelu vozidla byly odzkoušeny algoritmy adaptivniho tempomatu, včetně systemu Stop&Go a sledovani trajektorie vpředu jedouciho vozidla. Pomoci podobnostniho teoremu je možne vysledky přepočitat na skutečne vozidlo a předpovidat tak chovani vozidla v koloně, či stabilitu kolony vozidel.

Sledování obecného objektu na zařízení s omezenými prostředky v reálném čase je obtížné. Mnoho algoritmů věnujících se této problematice již existuje. V této práci se s nimi seznámíme. Různé přístupy k této problematice jsou diskutovány včetně hlubokého učení. Představeny jsou reprezentace objektu, datasety i metriky pro vyhodnocování. Mnoho sledovacích algorimů je představeno, osm z nich je implementováno a vyhodnoceno na VOT datasetu.

This diploma thesis deals with the tracking of objects using a robotic manipulator Epson C3 and a color camera. The work describes the basic qualities of the device to be used. The OpenCV library and its wrapper EmguCV are used as software tools for computer vision. It discusses the basic issues and principles of tracking objects in the image and introduces some methods of tracking. These methods have been tested and therefore their strengths and weaknesses, which appeared during testing, are listed here. Furthermore, there is a procedure for calculating the new coordinates of the camera and the manipulator effector using homogeneous transformations. The work contains the results of testing the algorithms and their evaluation. The output of the work is a test application for the Epson C3 robot.

The thesis deals with optimization of network flow monitoring. Flow-based network traffic processing, that is, processing packets based on some state information associated to the flows which the packets belong to, is a key enabler for a variety of network services and applications. The number of simultaneous flows increases with the growing number of new services and applications. It has become a challenge to keep a state per each flow in a network device processing high speed traffic. A flow table, a structure with flow states, must be stored in a memory hierarchy. The memory closest to the processing is known as a flow cache. Flow cache management plays an important role in terms of its effective utilization, which affects the performance of the whole system. This thesis focuses on an automated design of cache replacement policy optimized to a deployment on particular networks. A genetic algorithm is proposed to automate this process. The genetic algorithm generates and evaluates evolved replacement policies by a simulation on obtained traffic traces. The proposed algorithm is evaluated by designing replacement policies for two variations of the cache management problem. The first variation is an evolution of the replacement policy with an overall low number of state evictions from the flow cache. The second variation represents an evolution of the replacement policy with a low number of evictions belonging to large flows only. Optimized replacement policies for both variations are found while experimenting with various encoding of the replacement policy and genetic operators. The newly evolved replacement policies achieve better results than other tested policies. The evolved replacement policy lowers the overall amount of evictions by ten percent in comparison with the best compared policy. The evolved replacement policy focusing on large flows lowers the amount of their evictions two times. Moreover, no eviction occurs for most of the large flows (over 90%). The evolved replacement policy offers better resilience against flooding the flow cache with large amount of short flows which are typical side effects of scanning or distributed denial of service activities. An extension of the replacement policy is also proposed. The extension complements the replacement policy with an additional information extracted from packet headers. The results show further decrease in the number of evictions when the extension is used.

The aim of this thesis is a description and implementation of algorithms of the tracked objects in the video feed. This thesis introduces Mean shift and Continuously adaptive mean shift algorithms which represent category based on kernel tracking. For construction of a model is used a threedimensional color histogram whose construction is described in this thesis as well. The achievements of described algorithms are compared in the testing images sequences and evaluated in details.

This master’s thesis is dedicated to user’s movement tracking in public transport, as well as to optimize route planning. In the first part, author presents information related to graph theory and graph theory algorithms, including Breadth-First Search, Djikstra’s algorithm and A*. In the second part of this thesis, author describes the design of server and mobile application including description of routing process and how individual parts works.

Thesis deals with ultrasonographic contrast examinations, that are performed to assess tissue perfusion and non-invasive ultrasound method speckle tracking, overcoming the weaknesses of Doppler techniques used to scanning the movement of the tissue.

The doctoral thesis deals with spectrum sensing and subsequent use of the frequency spectrum by multicarrier communication system, which parameters are set on the basis of the optimization technique. Adaptation settings can be made with respect to several requirements as well as state and occupancy of individual communication channels. The system, which is characterized above is often referred as cognitive radio. Equipments operating on cognitive radio principles will be widely used in the near future, because of frequency spectrum limitation. One of the main contributions of the work is the novel usage of the Kolmogorov – Smirnov statistical test as an alternative detection of primary user signal presence. The new fitness function for Particle Swarm Optimization (PSO) has been introduced and the Error Vector Magnitude (EVM) parameter has been used in the adaptive greedy algorithm and PSO optimization. The dissertation thesis also incorporates information about the reliability of the frequency spectrum sensing in the modified greedy algorithm. The proposed methods are verified by the simulations and the frequency domain energy detection is implemented on the development board with FPGA.

The thesis deals with automatic control the font size by the distance from the reader. It includes theoretical acquaintance with the face detection and subsequent tracking of the detected area during the scene. Furthermore, there is a comparison of the tracking algorithms. Then the calculation of distance is decribed. It is based on the user’s calibration and based on the outcome occurs the font size is automatically corrected. There is also a description of a separate application Automatical controller of the text size, with the recommended settings of the program.

The aim of the master thesis is to derive and implement image porcessing methods for people detection and tracking in images or videos. The overall solution was chosen as a combination of modern approaches and methods which were recently presented. The proposed algorithm is able to create trajectory of the person moving in indoor building spaces even under influence of full or partial occlusion for a short period of time. The scene of interest is surveyed by a static camera having direct view on targets. Selected methods are implemented in C# programming language based on OpenCV library. Graphical user interface was created to show the final output of algorithm.

Abstract. This thesis presents a software library for eye gaze tracking. The typical use case is a person watching their computer screen. All data is obtained from a single video camera and is processed in real time. The resulting software is freely available including its source code.