An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

AbstractParticle filter algorithm is widely used for target tracking using video sequences, which is of great importance for intelligent surveillance applications. However, there is still much room for improvement, e.g. the so-called “sample impoverishment”. It is brought by re-sampling which aims to avoid particle degradation, and thus becomes the inherent shortcoming of the particle filter. In order to solve the problem of sample impoverishment, increase the number of meaningful particles and ensure the diversity of the particle set, an evolutionary particle filter with the immune genetic algorithm (IGA) for target tracking is proposed by adding IGA in front of the re-sampling process to increase particle diversity. Particles are regarded as the antibodies of the immune system, and the state of target being tracked is regarded as the external invading antigen. With the crossover and mutation process, the immune system produces a large number of new antibodies (particles), and thus the new particles can better approximate the true state by exploiting new areas. Regulatory mechanisms of antibodies, such as promotion and suppression, ensure the diversity of the particle set. In the proposed algorithm, the particle set optimized by IGA can better express the true state of the target, and the number of meaningful particles can be increased significantly. The effectiveness and robustness of the proposed particle filter are verified by target tracking experiments. Simulation results show that the proposed particle filter is better than the standard one in particle diversity and efficiency. The proposed algorithm can easily be extended to multiple objects tracking problems with occlusions

A STATE VECTOR AUGMENTATION METHOD FOR INCLUDING VELOCITY INFORMATION IN THE LIKELIHOOD FUNCTION OF THE SIR VIDEO TARGET TRACKING FILTER

This thesis is focused on visual target tracking. Visual target tracking has been widely studied. The main idea is to be able to determine the target's location from a video sequence. Techniques such as the Kalman Filter and its variations have been proved to be the optimal solution when the system is linear or can be linearized, and Gaussianity can be assumed. But these conditions often do not hold in real world applications. Therefore, an alternative approach based on Sequential Monte-Carlo methods, also known as the Particle Filter, arose among others and has become a popular technique for target tracking recently. The particle filter is able to estimate the target state under nonlinear, non-Gaussian conditions. Different types of particle filters have been developed over the years, but one of the most popular is the sampling importance resampling (SIR) algorithm. However, in conditions of highly structured clutter and occlusion the filter's performance is decreased and the tracker can lock into the background and loose the target. Since motion information has been shown to be very important for the unmanned target tracking problem, in this thesis I introduce a new method to make the SIR filter more robust against these conditions by indirectly including velocity information in the likelihood function of the SIR filter. I propose augmenting the SIR filter state vector in order to use particle velocity information to prevent particles with poor motion estimates from obtaining large weights. The main original contributions of this thesis include the following: * I developed the theoretical formulation for the State Vector Augmented SIR filter algorithm. * I reformulated the normalized cross correlation used in the Likelihood function of the SIR filter to include the velocity information in it. * I developed an algorithm to generate synthetic data sequences with targets that can change both in magnification and rotation for testing the efficacy of tracking algorithms in a controlled environment. * I developed a simple template update strategy to deal with target appearance changes. * I prove the effectiveness of the proposed algorithm with tracking results obtained from two longwave infrared sequences and two synthetic data sequences. The results show that this new method can improve tracking performance for moving targets immersed in strong structured clutter

Particle Filter with Binary Gaussian Weighting and Support Vector Machine for Human Pose Interpretation

Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine isproposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonizedusing thinning algorithm and classified using Support Vector Machine. The classification is to identify human pose,whether a normal or abnormal behavior. Here Particle filter is modified through weight calculation using Gaussiandistribution to reduce the computational time. The modified particle filter consists of four main phases. First, particlesare generated to predict target’s location. Second, weight of certain particles is calculated and these particles are used tobuild Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, updateparticles based on each weight. The modified particle filter could reduce computational time of object tracking sincethis method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method buildsGaussian distribution and calculates particle’s weight using this distribution. Through experiment using video datataken in front of cashier of convenient store, the proposed method reduced computational time in tracking process until68.34% in average compare to the conventional one, meanwhile the accuracy of tracking with this new method iscomparable with particle filter method i.e. 90.3%. Combination particle filter with binary Gaussian weighting andsupport vector machine is promising for advanced early crime scene investigation

Bayesian-based techniques for tracking multiple humans in an enclosed environment

This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help of a particle filter, multiple video cues and a new interaction model. A Markov chain Monte Carlo particle filter (MCMC-PF) is used along with a new interaction model which helps in modeling interactions of multiple targets. This helps to overcome tracking failures due to occlusions. A new weighted Markov chain Monte Carlo (WMCMC) sampling technique is also proposed which assists in achieving a reduced tracking error. Although effective, to accommodate multiple measurements (pixels) produced by every target, this technique aggregates measurements into features which results in information loss. In the second contribution, a novel variational Bayesian clustering-based multi-target tracking framework is proposed which can associate multiple measurements to every target without aggregating them into features. It copes with complex inter-target occlusions by maintaining the identity of targets during their close physical interactions and handles efficiently a time-varying number of targets. The proposed multi-target tracking framework consists of background subtraction, clustering, data association and particle filtering. A variational Bayesian clustering technique groups the extracted foreground measurements while an improved feature based joint probabilistic data association filter (JPDAF) is developed to associate clusters of measurements to every target. The data association information is used within the particle filter to track multiple targets. The clustering results are further utilised to estimate the number of targets. The proposed technique improves the tracking accuracy. However, the proposed features based JPDAF technique results in an exponential growth of computational complexity of the overall framework with increase in number of targets. In the final work, a novel data association technique for multi-target tracking is proposed which more efficiently assigns multiple measurements to every target, with a reduced computational complexity. A belief propagation (BP) based cluster to target association method is proposed which exploits the inter-cluster dependency information. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed techniques are evaluated on benchmark data sets and their performance is compared with state-of-the-art techniques by using, quantitative and global performance measures

Particle filter using acceptance-rejection method with emphasis on the target tracking problem.

Tsang Yuk Fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 59-62).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- Sequential Monte Carlo --- p.5Chapter 2.1 --- Recursive Bayesian estimation --- p.7Chapter 2.2 --- Bayesian sequential importance sampling --- p.8Chapter 2.3 --- Sclcction of iiiipoitance function --- p.10Chapter 2.4 --- Particle filter --- p.12Chapter 3 --- Target tracking and data association --- p.15Chapter 3.1 --- Target tracking and its applications --- p.16Chapter 3.2 --- Data association and JPDA method --- p.16Chapter 4 --- Particle filter using the acceptance-rejection method --- p.21Chapter 4.1 --- Particle Filter using the acceptance-rejection method --- p.22Chapter 4.2 --- Modified accoptance-rcjoction algorithm --- p.24Chapter 4.3 --- Examples --- p.26Chapter 4.3.1 --- Example 1: One dimensional non-linear case --- p.26Chapter 4.3.2 --- Example 2: Bearings-only tracking example --- p.27Chapter 4.3.3 --- Example 3: Single-target tracking --- p.31Chapter 4.3.4 --- Example 4: Multi-target tracking --- p.33Chapter 4.4 --- A new importance weight for bearings-only tracking problem --- p.34Chapter 5 --- Conclusion --- p.4

Deep Convolutional Correlation Particle Filter for Visual Tracking

In this dissertation, we explore the advantages and limitations of the application of sequential Monte Carlo methods to visual tracking, which is a challenging computer vision problem. We propose six visual tracking models, each of which integrates a particle filter, a deep convolutional neural network, and a correlation filter. In our first model, we generate an image patch corresponding to each particle and use a convolutional neural network (CNN) to extract features from the corresponding image region. A correlation filter then computes the correlation response maps corresponding to these features, which are used to determine the particle weights and estimate the state of the target. We then introduce a particle filter that extends the target state by incorporating its size information. This model also utilizes a new adaptive correlation filtering approach that generates multiple target models to account for potential model update errors. We build upon that strategy to devise an adaptive particle filter that can decrease the number of particles in simple frames in which there is no challenging scenarios and the target model closely reflects the current appearance of the target. This strategy allows us to reduce the computational cost of the particle filter without negatively impacting its performance. This tracker also improves the likelihood model by generating multiple target models using varying model update rates based on the high-likelihood particles. We also propose a novel likelihood particle filter for CNN-correlation visual trackers. Our method uses correlation response maps to estimate likelihood distributions and employs these likelihoods as proposal densities to sample particles. Additionally, our particle filter searches for multiple modes in the likelihood distribution using a Gaussian mixture model. We further introduce an iterative particle filter that performs iterations to decrease the distance between particles and the peaks of their correlation maps which results in having a few more accurate particles in the end of iterations. Applying K-mean clustering method on the remaining particles determine the number of the clusters which is used in evaluation step and find the target state. Our approach ensures a consistent support for the posterior distribution. Thus, we do not need to perform resampling at every video frame, improving the utilization of prior distribution information. Finally, we introduce a novel framework which calculates the confidence score of the tracking algorithm at each video frame based on the correlation response maps of the particles. Our framework applies different model update rules according to the calculated confidence score, reducing tracking failures caused by model drift. The benefits of each of the proposed techniques are demonstrated through experiments using publicly available benchmark datasets

Improved Multi Target Tracking in MIMO Radar System Using New Hybrid Monte Carlo–PDAF Algorithm

This article deals with the multi-target tracking problem (MTT) in MIMO radar systems. As a result, this problem is now seen as a new technological challenge. Thus, in different tracking scenarios, measurements from sensors are usually subject to a complex data association issue. The MTT data association problem of assigning measurements-to-target or target-state-estimates becomes more complex in MIMO radar system, once the crossing target tracking scenario arises, hence the interference phenomenon may interrupt the received signal and miss the state estimation process. To avoid most of these problems, we have improved a new hybrid algorithm based on particle filter called “Monte Carlo” associated to Joint Probabilistic data Association filter (JPDAF), the whole approach named MC-JPDAF algorithm has been proposed to replace the traditional method as is known by the Extended KALMAN filter (EKF) combined with JPDAF method, such as EKF-JPDAF algorithm. The obtained experimental results showed a challenging remediation. Where, the MC-JPDAF converges towards the accurate state estimation. Thus, more efficient than EKF-JPDAF. The simulation results prove that the designed system meets the objectives set for MC-JPDA by referring to an experimental database using the MATLAB Software Development Framework