A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management

Predicting Multiple Target Tracking Performance for Applications on Video Sequences

This dissertation presents a framework to predict the performance of multiple target tracking (MTT) techniques. The framework is based on the mathematical descriptors of point processes, the probability generating functional (p.g.fl). It is shown that conceptually the p.g.fls of MTT techniques can be interpreted as a transform that can be marginalized to an expression that encodes all the information regarding the likelihood model as well as the underlying assumptions present in a given tracking technique. In order to use this approach for tracker performance prediction in video sequences, a framework that combines video quality assessment concepts and the marginalized transform is introduced. The multiple hypothesis tracker (MHT), Joint Probabilistic Data Association (JPDA), Markov Chain Monte Carlo (MCMC) data association, and the Probability Hypothesis Density filter (PHD) are used as a test cases. We introduce their transforms and perform a numerical comparison to predict their performance under identical conditions. We also introduce the concepts that present the base for estimation in general and for applications in computer vision

To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, and MHT methods suffer from an opposite effect known as track repulsion. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm, and we argue that BP-based MTT exhibits significantly reduced track coalescence and no track repulsion. Our theoretical arguments are confirmed by numerical results.Comment: 13 page

Estimation, Decision and Applications to Target Tracking

This dissertation mainly consists of three parts. The first part proposes generalized linear minimum mean-square error (GLMMSE) estimation for nonlinear point estimation. The second part proposes a recursive joint decision and estimation (RJDE) algorithm for joint decision and estimation (JDE). The third part analyzes the performance of sequential probability ratio test (SPRT) when the log-likelihood ratios (LLR) are independent but not identically distributed. The linear minimum mean-square error (LMMSE) estimation plays an important role in nonlinear estimation. It searches for the best estimator in the set of all estimators that are linear in the measurement. A GLMMSE estimation framework is proposed in this disser- tation. It employs a vector-valued measurement transform function (MTF) and finds the best estimator among all estimators that are linear in MTF. Several design guidelines for the MTF based on a numerical example were provided. A RJDE algorithm based on a generalized Bayes risk is proposed in this dissertation for dynamic JDE problems. It is computationally efficient for dynamic problems where data are made available sequentially. Further, since existing performance measures for estimation or decision are effective to evaluate JDE algorithms, a joint performance measure is proposed for JDE algorithms for dynamic problems. The RJDE algorithm is demonstrated by applications to joint tracking and classification as well as joint tracking and detection in target tracking. The characteristics and performance of SPRT are characterized by two important functions—operating characteristic (OC) and average sample number (ASN). These two functions have been studied extensively under the assumption of independent and identically distributed (i.i.d.) LLR, which is too stringent for many applications. This dissertation relaxes the requirement of identical distribution. Two inductive equations governing the OC and ASN are developed. Unfortunately, they have non-unique solutions in the general case. They do have unique solutions in two special cases: (a) the LLR sequence converges in distributions and (b) the LLR sequence has periodic distributions. Further, the analysis can be readily extended to evaluate the performance of the truncated SPRT and the cumulative sum test

Predicting Multiple Target Tracking Performance for Applications on Video Sequences

This dissertation presents a framework to predict the performance of multiple target tracking (MTT) techniques. The framework is based on the mathematical descriptors of point processes, the probability generating functional (p.g.fl). It is shown that conceptually the p.g.fls of MTT techniques can be interpreted as a transform that can be marginalized to an expression that encodes all the information regarding the likelihood model as well as the underlying assumptions present in a given tracking technique. In order to use this approach for tracker performance prediction in video sequences, a framework that combines video quality assessment concepts and the marginalized transform is introduced. The multiple hypothesis tracker (MHT), Joint Probabilistic Data Association (JPDA), Markov Chain Monte Carlo (MCMC) data association, and the Probability Hypothesis Density filter (PHD) are used as a test cases. We introduce their transforms and perform a numerical comparison to predict their performance under identical conditions. We also introduce the concepts that present the base for estimation in general and for applications in computer vision

Distributed multiple model joint probabilistic data association with Gibbs sampling-aided implementation

This paper proposes a new distributed multiple model multiple manoeuvring target tracking algorithm. The proposed tracker is derived by combining joint probabilistic data association (JPDA) with consensus-based distributed filtering. Exact implementation of the JPDA involves enumerating all possible joint association events and thus often becomes computationally intractable in practice. We propose a computationally tractable approximation of calculating the marginal association probabilities for measurement-target mappings based on stochastic Gibbs sampling. In order to achieve scalability for a large number of sensors and high tolerance to sensor failure, a simple average consensus algorithm-based information JPDA filter is proposed for distributed tracking of multiple manoeuvring targets. In the proposed framework, the state of each target is updated using consensus-based information fusion while the manoeuvre mode probability of each target is corrected with measurement probability fusion. Simulations clearly demonstrate the effectiveness and characteristics of the proposed algorithm. The results reveal that the proposed formulation is scalable and much more efficient than classical JPDA without sacrificing tracking accurac

Fuzzy region assignment for visual tracking

In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects' features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.Research supported by projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB and CAM MADRINET S-0505/TIC/0255.publicad

Novel data association methods for online multiple human tracking

PhD ThesisVideo-based multiple human tracking has played a crucial role in many applications such as intelligent video surveillance, human behavior analysis, and health-care systems. The detection based tracking framework has become the dominant paradigm in this research eld, and the major task is to accurately perform the data association between detections across the frames. However, online multiple human tracking, which merely relies on the detections given up to the present time for the data association, becomes more challenging with noisy detections, missed detections, and occlusions. To address these challenging problems, there are three novel data association methods for online multiple human tracking are presented in this thesis, which are online group-structured dictionary learning, enhanced detection reliability and multi-level cooperative fusion. The rst proposed method aims to address the noisy detections and occlusions. In this method, sequential Monte Carlo probability hypothesis density (SMC-PHD) ltering is the core element for accomplishing the tracking task, where the measurements are produced by the detection based tracking framework. To enhance the measurement model, a novel adaptive gating strategy is developed to aid the classi cation of measurements. In addition, online group-structured dictionary learning with a maximum voting method is proposed to estimate robustly the target birth intensity. It enables the new-born targets in the tracking process to be accurately initialized from noisy sensor measurements. To improve the adaptability of the group-structured dictionary to target appearance changes, the simultaneous codeword optimization (SimCO) algorithm is employed for the dictionary update. The second proposed method relates to accurate measurement selection of detections, which is further to re ne the noisy detections prior to the tracking pipeline. In order to achieve more reliable measurements in the Gaussian mixture (GM)-PHD ltering process, a global-to-local enhanced con dence rescoring strategy is proposed by exploiting the classi cation power of a mask region-convolutional neural network (R-CNN). Then, an improved pruning algorithm namely soft-aggregated non-maximal suppression (Soft-ANMS) is devised to further enhance the selection step. In addition, to avoid the misuse of ambiguous measurements in the tracking process, person re-identi cation (ReID) features driven by convolutional neural networks (CNNs) are integrated to model the target appearances. The third proposed method focuses on addressing the issues of missed detections and occlusions. This method integrates two human detectors with di erent characteristics (full-body and body-parts) in the GM-PHD lter, and investigates their complementary bene ts for tracking multiple targets. For each detector domain, a novel discriminative correlation matching (DCM) model for integration in the feature-level fusion is proposed, and together with spatio-temporal information is used to reduce the ambiguous identity associations in the GM-PHD lter. Moreover, a robust fusion center is proposed within the decision-level fusion to mitigate the sensitivity of missed detections in the fusion process, thereby improving the fusion performance and tracking consistency. The e ectiveness of these proposed methods are investigated using the MOTChallenge benchmark, which is a framework for the standardized evaluation of multiple object tracking methods. Detailed evaluations on challenging video datasets, as well as comparisons with recent state-of-the-art techniques, con rm the improved multiple human tracking performance