A novel FPGA design of a high dynamic human tracking system using particle filter

The human respiratory movement seriously affects the precision of the robotized radiotherapy treatment for lung cancer. In order to reduce the influence of respiratory movement, the most effective method is real-time tracking of respiratory tumor motion, which adopts tracking algorithm to predict the location of the tumor, and adjust the treatment beam to catch the motion of the target. This paper utilizes the particle filter as tracking algorithm to predict the tumor motion trajectory. Firstly, the principle and process of particle filter algorithm are introduced; Secondly, the probability correlation model based on UT transform are proposed as the observation model of the particle filter; Finally, the conventional particle filter and the UT transform modeling are used to predict the location of the tumor with respiratory motion. The effectiveness and accuracy of particle filter algorithm based on UT transform is verified by error analysis. The experiment results show that the particle filter algorithm based on UT transform has stronger robustness and higher prediction precision compared with the linear estimation and the traditional particle filter, so it can meet the requirements of real-time tumor tracking for radiotherapy robots.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

Achieving the detection and tracking of moving targets has been widely applied in all fields of today's society. Because of the shortcomings of traditional video tracking system, this paper proposes a novel method for designing video processing system based on hardware design of FPGA and DSP, and moving target in video can be detected and tracked by this system. In this system, DSP as the core of the system, it mainly completes the processing algorithms of video and image data, FPGA as a coprocessor, responsible for the completion of the processing of external data and logic. The hardware structure, link configuration, program code and other aspects of system are optimized. Finally, through the experiment, the input frame rate of video is 40frames/s, and the image resolution is 512pixels × 512pixels, median 16bites quantitative image sequence, the system can complete the relevant real-time detection and tracking algorithm and extract targets position of image sequences correctly. The results show that the advantage is that this system has powerful operation speed, real time, high accuracy and stability.

We report on the design of a real-time tracking system using precise space and time information of the hit. Tracking detectors with about 150 ps time resolution are already available and intense R&D is in progress to improve the performance. We describe the implementation of a real-time tracking algorithm in commercial FPGAs for a prototype tracking system based on 8 planes of silicon sensors used as a case study. We demonstrate that the use of the additional time information in the tracking algorithm allows to suppress noise hits while reducing fake track combinations and improving real-time track reconstruction. The system provides offline-like quality tracks with sub-microsecond latency and it is capable to determine the time of the track with picosecond resolution assuming 10 ps resolution for the hits.

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

To deal with the problem of ballistic reentry target trajectory tracking, a novel particle filter algorithm is proposed to mitigate the particle degeneracy. It can eliminate the inefficient particles by judging the degree of particle degradation at each time and generates new particles based on the estimation state values and their covariance according to the degeneracy ratio of particles. Compared with Standard Particle Filter (SPF) algorithm and Unscented Particle Filter (UPF) algorithm, simulation results show that the proposed algorithm can achieve better tracking performance with lower computational burden for the trajectory reentry target tracking model. Especially in the case of ballistic target maneuver, the performance stability of the proposed algorithm is even more significant.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

Visual tracking is one of the key tasks in computer vision. The particle filter algorithm has been extensively used to tackle this problem due to its flexibility. However the conventional particle filter uses system transition as the proposal distribution, frequently resulting in poor priors for the filtering step. The main reason is that it is difficult, if not impossible, to accurately model the target's motion. Such a proposal distribution does not take into account the current observations. It is not a trivial task to devise a satisfactory proposal distribution for the particle filter. In this paper we advance a general augmented particle filtering framework for designing the optimal proposal distribution. The essential idea is to augment a second filter's estimate into the proposal distribution design. We then show that several existing improved particle filters can be rationalised within this general framework. Based on this framework we further propose variant algorithms for robust and efficient visual tracking. Experiments indicate that the augmented particle filters are more efficient and robust than the conventional particle filter.

Most of the countries have paid great attention to flood water level monitoring and tracking because flood may damages people's life and property. Since flood water level fluctuate highly nonlinear, it is very difficult to predict the flood water level. The particle filter algorithm is well known as a very effective solution for handling nonlinear problems. Thus, in this paper, this algorithm is applied to predict the flood water level. There are many variations of particle filter. This paper proposes Sequential Importance Sampling (SIS) particle filter to solve the above mentioned problem. SIS is the basic particle filter. However, the problems with SIS particle filter are the particle degeneration phenomenon, when after a few iterations only a few particles have nonzero weight. So, Sampling Importance Resampling (SIR) particle filter is also introduced as the improved particle filter. From the simulation results using Matlab, SIR particle filter outperforms SIS particle filter by comparing the Root Mean Square Error (RMSE) value.

liuyujiao2005@126.com Abstract Aiming at a nonlinear/non-Gaussian filter problem, the data processing of a single-yarn strength testing system, a filtering method based on the particle filter algorithm is put forward. This paper expounds the principle, and the working process and the procedures of particle filter. It discusses in detail the application of particle filter in the single-yarn strength testing, the modeling of testing system state equation, and the implementation of this algorithm in a project. It also indicates that particle filter algorithm used to process single-yarn strength test data achieves a good effect and satisfactory filtering precision by simulated tests. Finally, this paper predicts that particle filter will play an important role in general data processing and analyzing. The detection of the yarn quality is very important for cotton production. Yarn strength is the reflection of inner quality of yarn and is the necessary condition of yarn's processing performance and end-use. The unit of single-yarn strength is Newton (N) per centimeter (CN). By stretching testing with a single-yarn strength tester, the yarn's characteristic parameters will be obtained, and the physical index of breaking strength, breaking elongation, fracture strength, fracture time and strength tensile curves can be determined, so the quality of yarn will be ascertained (1) . This paper majors in the research of the parameter of the single-yarn strength of yarn. The strength of yarn will increase with increasing within a certain twist; however the strength of yarn will decrease when the twist exceeds the critical value. The relationship between yarn elongation and tension is often nonlinear. When the yarn quality is tested by single-yarn strength tester, the interference noise of test environment in the industrial field is non-Gaussian. So the processing of single-yarn strength testing data is a typical nonlinear and non-Gaussian filtering problem. The traditional digital filtering methods normally only filter out the caused by single reason and can't achieve satisfactory results for complex noise. In order to solve this problem, a large number of nonlinear recursive filtering algorithm such as extended Kalman filtering (EKF), the modified gain Kalman filter (MGEKF), U Kalman filter (UKF) algorithm come forth in recent years, and these filters are based on particular assumption to ensure the optimality. The actual data is usually very complex, and contains many factors such as non-Gaussian, nonlinear, high dimensional and noise; in this case, the Kalman filter generally can't get the analytical solution. This is a common problem and appears in many different kinds of fields (2-11) . Now, a new sequential Monte Carlo particle filter algorithm based on Bayesian principles has been paid more attention. The particle filter technology realize the recursive Bayesian filter with non- parametric Monte Carlo simulation method, which can fit to any state space model and the nonlinear systems which can't be expressed by traditional Kalman filter, and its accuracy approach the accuracy of optimal estimation (12) . This paper places emphasis on introducing the principle and method of the particle filter, and the application of the nonlinear data processing.

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributedmemory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with a tool for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 TB of particle data, on 192 cores with 67% parallel efficiency.

To address the challenges of low accuracy and the difficulty in balancing a large field of view and long distance when tracking high-speed moving targets with a single sensor, an ROI adaptive digital zoom tracking method is proposed. In this paper, we discuss the impact of ROI on image processing and describe the design of the ROI adaptive digital zoom tracking system. Additionally, we construct an adaptive ROI update model based on normalized target information. To capture target changes effectively, we introduce the multi-scale regional measure and propose an improved particle filter algorithm, referred to as the improved multi-scale regional measure resampling particle filter (IMR-PF). This method enables high temporal resolution processing efficiency within a high-resolution large field of view, which is particularly beneficial for high-resolution videos. The IMR-PF can maintain high temporal resolution within a wide field of view with high resolution. Simulation results demonstrate that the improved target tracking method effectively improves tracking robustness to target motion changes and reduces the tracking center error by 20%, as compared to other state-of-the-art methods. The IMR-PF still maintains good performance even when confronted with various interference factors and in real-world scenario applications.

Small target detection and dynamic target continuous tracking are difficulties in the field of target detection and tracking respectively. The experimental results show that the smaller the target size is, the lower the detection accuracy is, the more dynamic the target is, and the lower the tracking accuracy is. This paper presents a high dynamic small target detection and tracking system which is composed of a yolov4 network, KCF tracker, and Kalman filter prediction algorithm. In this system, the detection network is divided into global and local levels, in which the global network is used for initial detection and re-detection after tracking failure, while the local detection network and tracker are used for real-time tracking of small targets, a more robust system is constructed. Compared with the original target detection network, this method weakens the influence of external factors such as the change of target shape and the fast-moving speed on the detection accuracy. The recognition rate of the small target is more than 96%, and the detection accuracy achieves 78%.

In the application of particle filter algorithm for target tracking in wireless sensor networks, an Auxiliary particle filter (APF) and Gaussian particle filter (GPF) are discussed to solve the particle degradation problem of Particle Filter (PF). By introducing an auxiliary variable and two rounds weighted processes, APF makes the particle weights stable and relaxes particle degradation. GPF uses a Gaussian density function to approximately estimate posterior probability distribution. It doesn’t require re-sampling, and there is no particle degradation. Therefore, three algorithm are applied to single-target tracking in wireless sensor networks. Finally, the comparison of three algorithm’s performance in target tracking is presented and the simulation results are also given. From these results we can see that APF has better accuracy than PF algorithm and GPF has better accuracy and real-time performance than PF algorithm.

In this paper, a novel methodology is proposed to solve the simultaneous localization and mapping (SLAM) problem of mobile robot with particle filter (PF) algorithm. Compared with Kalman filter (KF) and extended Kalman filter (EKF), PF has a better performance in non-linear non-Gaussian environments. A close-loop updating scheme is developed in which positions of the robot and landmarks are updated with particle filtering and a weighted averaging algorithm respectively, and are linked through an additional feedback and correction process. An adaptive re-sampling method is used to reduce the computational load. The results of the simulation indicate that the PF-SLAM algorithm can localize the robot and landmarks accurately, and the error of landmarks' estimation converges better than general SLAM algorithms.