Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking <s> Biologically inspired and social interaction models <s> The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle systems, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. <s> BIB001 </s> Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking <s> Biologically inspired and social interaction models <s> Since the subject of traffic dynamics has captured the interest of physicists, many astonishing effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by so-called ``phantom traffic jams'', although they all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction of the traffic volume cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize in lanes, while similar systems are ``freezing by heating''? Why do self-organizing systems tend to reach an optimal state? Why do panicking pedestrians produce dangerous deadlocks? All these questions have been answered by applying and extending methods from statistical physics and non-linear dynamics to self-driven many-particle systems. This review article on traffic introduces (i) empirically data, facts, and observations, (ii) the main approaches to pedestrian, highway, and city traffic, (iii) microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts like a general modelling framework for self-driven many-particle systems, including spin systems. Subjects such as the optimization of traffic flows and relations to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are discussed as well. <s> BIB002 </s> Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking <s> Biologically inspired and social interaction models <s> Cluster Tracking (also called group tracking) is a key approach to greatly reducing the required data communication and processing loads that can result from the extreme amount of ambiguous data that might be generated by radar and IR sensors in the early post boost phase of a ballistic missile. Cluster tracking is especially appropriate in tracking a mixture of resolved and unresolved objects as a cluster and simplifies the processing for initiating individual tracks when ::: many of the target measurements are resolved. This paper presents the bibliography that resulted from a literature search on cluster tracking using data from one or more sensors. Although the focus was on cluster tracking, the literature search also uncovered papers on formation tracking and track partitioning (track clustering) and those papers are included in the bibliography. The paper also includes an introduction that provides an overview of cluster tracking and the advantages of tracking clusters. This includes a discussion of the four major types of cluster tracking and the uses ::: and distinction between cluster tracking, formation tracking, and track clusters (partitions). <s> BIB003 </s> Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking <s> Biologically inspired and social interaction models <s> It is common practice to represent a target group (or an extended target) as set of point sources and attempt to formulate a tracking filter by constructing possible assignments between measurements and the sources. We suggest an alternative approach that produces a measurement model (likelihood) in terms of the spatial density of measurements over the sensor observation region. In particular, the measurements are modelled as a Poisson process with a spatially dependent rate parameter. This representation allows us to model extended targets as an intensity distribution rather than a set of points and, for a target formation, it gives the option of modelling part of the group as a spatial distribution of target density. Furthermore, as a direct consequence of the Poisson model, the measurement likelihood may be evaluated without constructing explicit association hypotheses. This considerably simplifies the filter and gives a substantial computational saving in a particle filter implementation. The Poisson target-measurement model will be described and its relationship to other filters will be discussed. Illustrative simulation examples will be presented. <s> BIB004 </s> Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking <s> Biologically inspired and social interaction models <s> This comprehensive resource provides you with an in-depth understanding of finite-set statistics (FISST) - a recently developed method which unifies much of information fusion under a single probabilistic, in fact Bayesian, paradigm. The book helps you master FISST concepts, techniques, and algorithms, so you can use FISST to address real-world challenges in the field. You learn how to model, fuse, and process highly disparate information sources, and detect and track non-cooperative individual/platform groups and conventional non-cooperative targets. You find a rigorous Bayesian unification for many aspects of expert systems theory. Moreover, the book presents systematic integral and differential calculus for multisource-multitarget problems, providing a methodology for devising rigorous new techniques. This accessible and detailed book is supported with over 3,000 equations, 90 clear examples, 70 explanatory figures, and 60 exercises with solutions. <s> BIB005 </s> Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking <s> Biologically inspired and social interaction models <s> In this paper, we study how a self-organized mobile robot flock can be steered toward a desired direction through externally guiding some of its members. Specifically, we propose a behavior by extending a previously developed flocking behavior to steer self-organized flocks in both physical and simulated mobile robots. We quantitatively measure the performance of the proposed behavior under different parameter settings using three metrics, namely, (1) the mutual information metric, adopted from Information Theory, to measure the information shared between the individuals during steering, (2) the accuracy metric from directional statistics to measure the angular deviation of the direction of the flock from the desired direction, and (3) the ratio of the largest aggregate to the whole flock and the ratio of informed individuals remaining with the largest aggregate, as a metric of flock cohesion. We conducted a systematic set of experiments using both physical and simulated robots, analyzed the transient and steady-state characteristics of steered flocking, and evaluate the parameter conditions under which a swarm can be successfully steered. We show that the experimental results are qualitatively in accordance with the ones that were predicted in Couzin et al. model (Nature, 433:513–516, 2005) and relate the quantitative differences to the differences between the models. <s> BIB006 </s> Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking <s> Biologically inspired and social interaction models <s> In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models within a continuous time setting using stochastic differential equations (SDE) that aim to mimic behavioural properties of groups. We also describe a possible way of modeling interactions between closely spaced targets using repulsive forces. These can be combined with a group structure transition model to create realistic evolving group models. We use a Markov chain Monte Carlo (MCMC)-particles algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups as well as to infer the correct group structure over time. The group tracking model is also applied to two sets of real ground moving target indicator (GMTI) radar data with group targets. The results show significant improvement in tracking accuracy over tracking without group models. <s> BIB007
Many of the group object tracking approaches are developed by seeking similarities with emerging behaviors in complex biological systems, such as flocking, swarming, herding and schooling. In such models BIB002 BIB003 BIB006 , local interaction rules maintain coordination among individuals. This in turn gives rise to a collective behavior which may be qualitatively different from the superposition of the separate components. A common modeling conception, known as Boids, embodies the following set of local interaction rules BIB001 : separation, alignment and cohesion. For instance, a flock of birds tends to move in a way to avoid crowding local flockmates and keeps a certain distance between each other, which is an example of separation. The group constituents tend to steer towards the average heading of local flockmates and hence achieves alignment. Lastly, cohesion sustains a certain amount of coherence of the flock motion as individuals tend to move towards the average position of local flockmates. The flocks of birds usually have a leader. This has inspired the development of the so called leader-follower models. The leader-follower model describes a behavior in which each member of a group interacts with an aggregative, yet virtual object. In BIB007 , this concept has been conveniently formulated in continuous time through a multivariate stochastic differential equation and then derived in discrete time without approximation errors, owing to the assumed linear and Gaussian form of the model. In particular, two different models are proposed. In the first, the basic group model and the group parameter are represented as a deterministic function of the objects. The second is a group model with a virtual leader. An additional state variable is introduced to characterize the group or bulk parameter. This second approach is closer in spirit to the bulk velocity model and virtual leader-follower model BIB005 . Such model provides a more flexible behavior since the virtual leader is no longer a deterministic function of the individual object states. Fig. 4 gives a graphical illustration of the restoring forces towards the virtual leader for a flock of four birds. Hierarchical learning models for activities with multiple interacting objects are proposed in  and their potential is illustrated by experiments in sea navigation. A broad range of models for pedestrians and crowds tracking are proposed in  . The spatio-temporal structure for the ith object in a group, as defined in BIB007 , is given by: (8) dv t /dt = −γ 2vt + dL t /dt. (9) Here x t,i is the Cartesian position in the x direction of the ith object of the group at continuous time t, withẋ t,i is the corresponding velocity; v t andv t represent respectively the Cartesian position and the velocity both in the X direction of the unobserved virtual leader of the group; W x t,i and L x t are two independent Brownian motion processes, with W t,i being assumed to be independently generated for each object i in the group, whereas L t is a noise component common to all members of a group. The parameters α and β are positive, and reflect the strength of the pull towards the group center. The terms γ 1ẋt,i and γ 2vt simply prevent the velocities of the object and the virtual leader drifting up to very large values with time. Finally, to avoid colocation or spatial collision of group components, an additional repulsive force r i is introduced in (8) when objects become too close. The positive constants γ 1 and γ 2 give different weights of the two terms in (8) and (9), respectively. The spatio-temporal model (8) and (9) can be used to define the prior transition of the objects in (6). The likelihood model for small groups is constructed in a way to deal with the measurement origin uncertainty. Since the likelihood calculation methods are similar to those for large groups and extended objects, efficient likelihood functions are presented in Section 4.1. Section 4 presents the key methodologies for large groups tracking and how the state pdf can capture group configurations. The Poisson likelihood model BIB004 is reviewed. The approach in which the extent of the group is considered as a random matrix is summarized next. The last part of Section 4 is devoted to the random finite set approach.