Particle Filter-based Tracking Algorithm Research Articles

Particle Filter–Based Target Tracking Algorithm for Magnetic Resonance–Guided Respiratory Compensation: Robustness and Accuracy Assessment

To assess overall robustness and accuracy of a modified particle filter-based tracking algorithm for magnetic resonance (MR)-guided radiation therapy treatments. An improved particle filter-based tracking algorithm was implemented, which used a normalized cross-correlation function as the likelihood calculation. With a total of 5 healthy volunteers and 8 patients, the robustness of the algorithm was tested on 24 dynamic magnetic resonance imaging (MRI) time series with varying resolution, contrast, and signal-to-noise ratio. The complete data set included data acquired with different scan parameters on a number of MRI scanners with varying field strengths, including the 1.5TMR linear accelerator. Tracking errors were computed by comparing the results obtained by the particle filter algorithm with experts' delineations. The ameliorated tracking algorithm was able to accurately track abdominal as well as thoracic tumors, whereas the previous Bhattacharyya distance-based implementation failed in more than 50% of the cases. The tracking error, combined over all MRI acquisitions, is 1.1±0.4 mm, which demonstrated high robustness against variations in contrast, noise, and image resolution. Finally, the effect of the input/control parameters of the model was very similar across all cases, suggesting a class-based optimization is possible. The modified particle filter tracking algorithm is highly accurate and robust against varying image quality. This makes the algorithm a promising candidate for automated tracking on the MR linear accelerator.

Thermal Tracking in Mobile Robots for Leak Inspection Activities

Maintenance tasks are crucial for all kind of industries, especially in extensive industrial plants, like solar thermal power plants. The incorporation of robots is a key issue for automating inspection activities, as it will allow a constant and regular control over the whole plant. This paper presents an autonomous robotic system to perform pipeline inspection for early detection and prevention of leakages in thermal power plants, based on the work developed within the MAINBOT (http://www.mainbot.eu) European project. Based on the information provided by a thermographic camera, the system is able to detect leakages in the collectors and pipelines. Beside the leakage detection algorithms, the system includes a particle filter-based tracking algorithm to keep the target in the field of view of the camera and to avoid the irregularities of the terrain while the robot patrols the plant. The information provided by the particle filter is further used to command a robot arm, which handles the camera and ensures that the target is always within the image. The obtained results show the suitability of the proposed approach, adding a tracking algorithm to improve the performance of the leakage detection system.

An Efficient Particle Filter–based Tracking Method Using Graphics Processing Unit (GPU)

Particle filter has been proven very robust in handling non-linear and non-Gaussian problems and has been widely used in the area of object tracking. One of the main problems in particle filter-based object tracking is, however, its high computational cost induced by the most time-consuming stage of measurement model computation. This paper makes progress in resolving the problem by proposing an efficient particle filter-based tracking algorithm using color information. First, a compact color cooccurrence histogram is presented, which considers both spatial and color information and can effectively represent color distribution with a very small number of histogram bins. The paper also introduces integral images by which the cooccurrence histogram can be obtained with simple array reference operations. However, the construction of the integral images on the CPU may be computationally expensive. Hence, this paper develops parallel algorithms on a desktop Graphics Processing Unit (GPU), which accomplishes the integral images construction and cooccurrence histogram computation after bin index determination. The resulting algorithm is quite efficient and has better performance than the traditional histogram-based tracking algorithm. The tracking time of the proposed algorithm increases insignificantly with the growth of particle number, and it remains consistent among varying image sequences and stable throughout all frames in the same image sequence due to its irrelevance to object size. Experiments in diverse image sequences validate our conclusions.

Design and implementation of embedded computer vision systems based on particle filters

Particle filtering methods are gradually attaining significant importance in a variety of embedded computer vision applications. For example, in smart camera systems, object tracking is a very important application and particle filter based tracking algorithms have shown promising results with robust tracking performance. However, most particle filters involve vast amount of computational complexity, thereby intensifying the challenges faced in their real-time, embedded implementation. Many of these applications share common characteristics, and the same system design can be reused by identifying and varying key system parameters and varying them appropriately. In this paper, we present a System-on-Chip (SoC) architecture involving both hardware and software components for a class of particle filters. The framework uses parameterization to enable fast and efficient reuse of the architecture with minimal re-design effort for a wide range of particle filtering applications as well as implementation platforms.Using this framework, we explore different design options for implementing three different particle filtering applications on field-programmable gate arrays (FPGAs). The first two applications involve particle filters with one-dimensional state transition models, and are used to demonstrate the key features of the framework. The main focus of this paper is on design methodology for hardware/software implementation of multi-dimensional particle filter application and we explore this in the third application which is a 3D facial pose tracking system for videos. In this multi-dimensional particle filtering application, we extend our proposed architecture with models for hardware/software co-design so that limited hardware resources can be utilized most effectively. Our experiments demonstrate that the framework is easy and intuitive to use, while providing for efficient design and implementation. We present different memory management schemes along with results on trade-offs between area (FPGA resource requirement) and execution speed.

Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering

Mobile robots in real-life settings would benefit from being able to localize and track sound sources. Such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. To give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. In this paper we propose a robust sound source localization and tracking method using an array of eight microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. Results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 m. These new capabilities allow a mobile robot to interact using more natural means with people in real-life settings.