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Content available remote An adaptive window object tracking algorithm based on variable resolution
100%
EN
This paper presents an adaptive window object tracking method based on variable resolution. It copes with the change in size of the object during visual tracking. On the basis of the visual tracking algorithm, based on maximum posterior probability, we analyze the posterior probability index on the inside and outside panes of the object window, then build a mathematical model for adjusting object size with an adaptive window. Since the resolution changes according to the size of the object, this thesis uses a statistical sampling method of the feature by variable resolution. The resolution of the statistical feature is correspondingly changed in object tracking with an adaptive window. The resolution of a larger object is decreased, which realizes an object tracking method with adaptive window based on variable resolution.
EN
Presently the interaction of robots with human plays an important role in various social applications. Reliable tracking is an important aspect for the social robots where robots need to follow the moving person. This paper proposes the implementation of automated tracking and real time following algorithm for robotic automation. Occlusion and identity retention are the major challenges in the tracking process. Hence, a feature set based identity retention algorithm is used and integrated with robot operating system. The tracking algorithm is implemented using robot operating system in Linux and using OpenCV. The tracking algorithm achieved 85% accuracy and 72.30% precision. Further analysis of tracking algorithm corresponds to the integration of ROS and OpenCV is presented. The analysis of tracking algorithm concludes that ROS linking required 0.64% more time in comparison with simple OpenCV code based tracking algorithm.
3
Content available remote A coarse-to-fine kernel matching approach for mean-shift based visual tracking
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EN
Mean shift is an efficient pattern match algorithm. It is widely used in visual tracking fields since it need not perform whole search in the image space. It employs gradient optimization method to reduce the time of feature matching and realize rapid object localization, and uses Bhattacharyya coefficient as the similarity measure between object template and candidate template. This thesis presents a mean shift algorithm based on coarse-to-fine search for the best kernel matching. This paper researches for object tracking with large motion area based on mean shift. To realize efficient tracking of such an object, we present a kernel matching method from coarseness to fine. If the motion areas of the object between two frames are very large and they are not overlapped in image space, then the traditional mean shift method can only obtain local optimal value by iterative computing in the old object window area, so the real tracking position cannot be obtained and the object tracking will be disabled. Our proposed algorithm can efficiently use a similarity measure function to realize the rough location of motion object, then use mean shift method to obtain the accurate local optimal value by iterative computing, which successfully realizes object tracking with large motion. Experimental results show its good performance in accuracy and speed when compared with background-weighted histogram algorithm in the literature.
EN
In this paper, we present a method for extracting of mobile robots in a sequence of noisy frames, assuming a complex background composed of textured floor, illuminated unvenly. A homomorphic filter if used, as a preprocessor, to enhance the acquired frames by eliminating the illumination component and emphasizing the reflectance component of the image function. To speed up preprocessing of each frame, filtering is only applied to the pixels belonging to the regions of interest (ROI). In all the tested cases, homomorphic--filtering led to better results than those obtained without preprocessing. The segmentation process has been based on seeded region growing procedure for reconstructing the shape of the mobil robot. We proposed automatic seed points selection in the binarized difference image, and use an adaptive threshold. This use eliminates or at least considerably reduces false negative detections, and reduces sensitivity of aggregation results to the selected seed points as compared to the classical seeded region growing procedure. Additionally, by imposing a condition of strong connectivityu bettween a seed point and its neighborhood, aggregation of undesired pixels efficiently eliminates false positive detections. Implementation of segmentation and tracking can be run in real time. High tracking accuracy has been obtained through out all the frames in a test sequence.
Drogownictwo
|
2021
|
tom nr 1
32, III–IV okł.
PL
W artykule, na wstępie, wymieniono wyniki eksperymentów naukowych, które identyfikują wpływ poziomu koncentracji kierującego do odbioru informacji o ruchu i o drodze na bezpieczeństwo ruchu. W nawiązaniu do tych wyników autor sugeruje, aby w planowanych eksperymentach rozważyć wykorzystanie urządzenia Eyetraker do analizy procesu śledzenia wzrokowego drogi i ruchu przez kierującego w czasie jazdy. Pozytywne rezultaty takiego eksperymentu umożliwiłyby zastosowanie tego urządzenia, wykorzystywanego obecnie w wielu dziedzinach, jako wspomaganie pracy kierującego samochodem, przyczyniając się do ograniczenia wypadków drogowych, wywołanych przez kierujących w ruchu drogowym jak np. przez tzw. przyśniecie kierującego podczas jazdy.
EN
The article, at the outset, lists the results of scientific experiments that identify the impact of the level of concentration (exactly distraction) of the driver’s attention (and thus, indirectly, the use of eyesight) to the perception of traffic and road information by the driver, on traffic safety. With reference to these results, the author suggests that in the planned experiments or possibly new, consider using the Eyetracker device to analyse the process of visual tracking of the road and traffic by the driver while driving. Perhaps the positive results of this experiment would make it possible to use this device, which is currently used in many areas, to support the work of the driver, contributing to the reduction of road accidents caused by drivers in traffic, such as by the so-called the driver’s snowing while driving.
EN
The problem of the real time estimation of the position and orientation of moving objects for position-based visual servoing control of robotic systems is considered in this paper. A computationally efficient algorithm is proposed based on Kalman filtering of the visual measurements of the position of suitable feature points selected on the target objects. The efficiency of the algorithm is improved by adopting a pre-selection technique of the feature points, based on Binary Space Partitioning (BSP) tree geometric models of the target objects, which takes advantage of the Kalman folter prediction capability. Computer simulations are presented to test the performance of the estimation algorithm in the presence of noise, different types of lens geometric distortion, quantization and calibration errors.
7
Content available remote An improved real visual tracking system using particle filter
63%
EN
This paper presents a real hybrid visual tracking system based on a special-color model of target with improving the performance of this designed visual tracking system using various linear and nonlinear estimators like Kalman filter and particle filter. Moreover, the whole system has been designed and implemented in the laboratory by fusing the tracking algorithm that was created utilizing python software with a moving camera sensor. In addition, the designed visual tracking system has been simple, low cost and achieved all stages of visual tracking process like target initialization, appearance modeling, movement estimation, and target positioning with a great success. Finally, the graphical analysis results of the designed system illustrated had a great illustration on the validity of utilizing particle filer was very efficient and clearer with maneuvering targets than that were used with Kalman filter.
PL
W niniejszym artykule przedstawiono hybrydowy system śledzenia wizualnego oparty na modelu celu w specjalnej kolorystyce z poprawą wydajności tego zaprojektowanego systemu śledzenia wizualnego przy użyciu różnych liniowych i nieliniowych estymatorów, takich jak filtr Kalmana i filtr cząsteczkowy. Co więcej, cały system został zaprojektowany i wdrożony w laboratorium poprzez połączenie algorytmu śledzenia, który został stworzony przy użyciu oprogramowania Pythona z ruchomym czujnikiem kamery. Ponadto zaprojektowany system śledzenia wizualnego był prosty, tani i osiągnął z dużym sukcesem wszystkie etapy procesu śledzenia wizualnego, takie jak inicjalizacja celu, modelowanie wyglądu, szacowanie ruchu i pozycjonowanie celu. Wreszcie, wyniki analizy graficznej zilustrowanego zaprojektowanego systemu doskonale ilustrują zasadność wykorzystania filtra cząstek, który był bardzo wydajny i wyraźniejszy w przypadku celów manewrujących niż ten, który był używany z filtrem Kalmana.
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