Mean field approach for tracking similar objects

In this paper, we consider the problem of tracking similar objects. We show how a mean field approach can be used to deal with interacting targets and we compare it with Markov Chain Monte Carlo (MCMC). Two mean field implementations are presented. The first one is more general and uses particle filtering. We discuss some simplifications of the base algorithm that reduce the computation time. The second one is based on suitable Gaussian approximations of probability densities that lead to a set of self-consistent equations for the means and covariances. These equations give the Kalman solution if there is no interaction. Experiments have been performed on two kinds of sequences. The first kind is composed of a single long sequence of twenty roaming ants and was previously analysed using MCMC. In this case, our mean field algorithms obtain substantially better results. The second kind corresponds to selected sequences of a football match in which the interaction avoids tracker coalescence in situations where independent trackers fail.

Gaussian Approximation for Tracking Occluding and Interacting Targets

In this paper, we show how interacting and occluding targets can be tackled successfully within a Gaussian approximation. For that purpose, we develop a general expansion of the mean and covariance of the posterior and we consider a first order approximation of it. The proposed method differs from EKF in that neither a non-linear dynamical model nor a non-linear measurement vector to state relation have to be defined, so it works with any kind of interaction potential and likelihood. The approach has been tested on three sequences (10400, 2500, and 400 frames each one). The results show that our approach helps to reduce the number of failures without increasing too much the computation time with respect to methods that do not take into account target interactions.

Animal foraging from an individual perspective: an object orientated model

A model system, HOOFS (Hierarchical Object Orientated Foraging Simulator), has been developed to study foraging by animals in a complex environment. The model is implemented using an individual-based object-orientated structure. Different species of animals inherit their general properties from a generic animal object which inherits from the basic dynamic object class. Each dynamic object is a separate program thread under the control of a central scheduler. The environment is described as a map of small hexagonal patches, each with their own level of resources and a patch-specific rate of resource replenishment. Each group of seven patches (0th order) is grouped into a Ist order super-patch with seven nth order super-patches making up a n + 1th order super-patch for n up to a specified value. At any time each animal is associated with a single patch. Patch choice is made by combining the information on the resources available within different order patches and super-patches along with information on the spatial location of other animals. The degree of sociality of an animal is defined in terms of optimal spacing from other animals and by the weighting of patch choice based on social factors relative to that based on food availability. Information, available to each animal, about patch resources diminishes with distance from that patch. The model has been used to demonstrate that social interactions can constrain patch choice and result in a short-term reduction of intake and a greater degree of variability in the level of resources in patches. We used the model to show that the effect of this variability on the animal's intake depends on the pattern of patch replenishment. (C) 1998 Elsevier Science B.V. All rights reserved.</p>

Tracking the Links to Social Functioning: Asperger Syndrome and Gaze Patterns

The application of Eye Tracking (ET) to the study of social functioning in Asperger Syndrome (AS) provides a unique perspective into social attention and cognition in this atypical neurodevelopmental group. Research in this area has shown how ET can capture social attention atypicalities within this group, such as diminished fixations to the eye region when viewing still images and movie clips; increased fixation to the mouth region; reduced face gaze. Issues exist, however, within the literature, where the type (static/dynamic) and the content (ecological validity) of stimuli used appear to affect the nature of the gaze patterns reported. Objectives: Our research aims were: using the same group of adolescents with AS, to compare their viewing patterns to age and IQ matched typically developing (TD) adolescents using stimuli considered to represent a hierarchy of ecological validity, building from static facial images; through a non-verbal movie clip; through verbal footage from real-life conversation; to eye tracking during real-life conversation. Methods: Eleven participants with AS were compared to 11 TD adolescents, matched for age and IQ. In Study 1, participants were shown 2 sets of static facial images (emotion faces, still images taken from the dynamic clips). In Study 2, three dynamic clips were presented (1 non-verbal movie clip, 2 verbal footage from real-life conversation). Study 3 was an exploratory study of eye tracking during a real-life conversation. Eye movements were recorded via a HiSpeeed (240Hz) SMI eye tracker fitted with chin and forehead rests. Various methods of analysis were used, including a paradigm for temporal analysis of the eye movement data. Results: Results from these studies confirmed that the atypical nature of social attention in AS was successfully captured by this paradigm. While results differed across stimulus sets,
collectively they demonstrated how individuals with AS failed to focus on the most socially relevant aspects of the various stimuli presented. There was also evidence that the eye movements of the AS group were atypically affected by the presence of motion and verbal information. Discriminant Function Analysis demonstrated that the ecological validity of stimuli was an important factor in identifying atypicalities associated with AS, with more accurate classifications of AS and TD groups occurring for more naturalistic stimuli (dynamic rather than static). Graphical analysis of temporal sequences of eye movements revealed the atypical manner in which AS participants followed interactions within the dynamic stimuli. Taken together with data on the order of gaze patterns, more subtle atypicalities were detected in the gaze behaviour of AS individuals towards more socially pertinent regions of the dynamic stimuli. Conclusions: These results have potentially important implications for our understanding of deficits in Asperger Syndrome, as they show that, with more naturalistic stimuli, subtle differences in social attention can be detected that

TracEE: Tracking Ecopreneurial Enterprises

Website detailing findings from TracEE research project and case examples

Migratory navigation in birds: new opportunities in an era of fast-developing tracking technology

Birds have remained the dominant model for studying the mechanisms of animal navigation for decades, with much of what has been discovered coming from laboratory studies or model systems. The miniaturisation of tracking technology in recent years now promises opportunities for studying navigation during migration itself (migratory navigation) on an unprecedented scale. Even if migration tracking studies are principally being designed for other purposes, we argue that attention to salient environmental variables during the design or analysis of a study may enable a host of navigational questions to be addressed, greatly enriching the field. We explore candidate variables in the form of a series of contrasts (e. g. land vs ocean or night vs day migration), which may vary naturally between migratory species, populations or even within the life span of a migrating individual. We discuss how these contrasts might help address questions of sensory mechanisms, spatiotemporal representational strategies and adaptive variation in navigational ability. We suggest that this comparative approach may help enrich our knowledge about the natural history of migratory navigation in birds.

Resistance or Resilience? Tracking the Pathway of Recent Arrivals to a ‘New’ Rural Destination

Recent patterns of migration indicate that international migrants are not confined to urban gateways. Instead many migrants have settled in new destination areas located in rural and small town areas. While this might appear to be a positive phenomenon for rural areas struggling with decline and stagnation, the reality is that many of these areas are ill-equipped to manage the rate and pace of change that has been witnessed in recent years. Migration to established, typically urban areas has been the subject of extensive research. However, little is known about the way in which migrants navigate their way through social structures as they settle into destinations with little experience of immigration. Using empirical research, this article considers the way in which migrants navigate their way through social structures to establish life in a so-called ‘new’ migration destination. It analyses the way in which government and civil society respond to their needs of recent arrivals, showing how both NGO’s and the statutory sector play an important role in this process. It considers the ramifications for these different sectors and the implications for so-called ‘new’ destinations as they become more established or ‘mature’ areas of immigration.

Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.