Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity

This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context

Video trajectory analysis using unsupervised clustering and multi-criteria ranking

Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Learning object behaviour models

The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models. The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy. This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences. The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine

Unsupervised modelling of a transitional boundary layer

A data-driven approach for the identification of local turbulent-flow states and of their dynamics is proposed. After subdividing a flow domain in smaller regions, the K -medoids clustering algorithm is used to learn from the data the different flow states and to identify the dynamics of the transition process. The clustering procedure is carried out on a two-dimensional (2-D) reduced-order space constructed by the multidimensional scaling (MDS) technique. The MDS technique is able to provide meaningful and compact information while reducing the dimensionality of the problem, and therefore the computational cost, without significantly altering the data structure in the state space. The dynamics of the state transitions is then described in terms of a transition probability matrix and a transition trajectory graph. The proposed method is applied to a direct numerical simulation dataset of an incompressible boundary layer flow developing on a flat plate. Streamwise-spanwise velocity fields at a specific wall-normal position are referred to as observations. Reducing the dimensionality of the problem allows us to construct a 2-D map, representative of the local turbulence intensity and of the spanwise skewness of the turbulence intensity in the observations. The clustering process classifies the regions containing streaks, turbulent spots, turbulence amplification and developed turbulence while the transition matrix and the transition trajectories correctly identify the states of the process of bypass transition.This work has been supported by: (i) the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (PITUFLOW-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation); (ii) the COTURB project (Coherent Structures in Wall-bounded Turbulence), funded by the European Research Council (ERC), under grant ERC-2014.AdG-669505

Model checking learning agent systems using Promela with embedded C code and abstraction

As autonomous systems become more prevalent, methods for their verification will become more widely used. Model checking is a formal verification technique that can help ensure the safety of autonomous systems, but in most cases it cannot be applied by novices, or in its straight \off-the-shelf" form. In order to be more widely applicable it is crucial that more sophisticated techniques are used, and are presented in a way that is reproducible by engineers and verifiers alike. In this paper we demonstrate in detail two techniques that are used to increase the power of model checking using the model checker SPIN. The first of these is the use of embedded C code within Promela specifications, in order to accurately re ect robot movement. The second is to use abstraction together with a simulation relation to allow us to verify multiple environments simultaneously. We apply these techniques to a fairly simple system in which a robot moves about a fixed circular environment and learns to avoid obstacles. The learning algorithm is inspired by the way that insects learn to avoid obstacles in response to pain signals received from their antennae. Crucially, we prove that our abstraction is sound for our example system { a step that is often omitted but is vital if formal verification is to be widely accepted as a useful and meaningful approach

Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction

The emerging field of action prediction plays a vital role in various computer vision applications such as autonomous driving, activity analysis and human-computer interaction. Despite significant advancements, accurately predicting future actions remains a challenging problem due to high dimensionality, complex dynamics and uncertainties inherent in video data. Traditional supervised approaches require large amounts of labelled data, which is expensive and time-consuming to obtain. This paper introduces a novel self-supervised video strategy for enhancing action prediction inspired by DINO (self-distillation with no labels). The Temporal-DINO approach employs two models; a 'student' processing past frames; and a 'teacher' processing both past and future frames, enabling a broader temporal context. During training, the teacher guides the student to learn future context by only observing past frames. The strategy is evaluated on ROAD dataset for the action prediction downstream task using 3D-ResNet, Transformer, and LSTM architectures. The experimental results showcase significant improvements in prediction performance across these architectures, with our method achieving an average enhancement of 9.9% Precision Points (PP), highlighting its effectiveness in enhancing the backbones' capabilities of capturing long-term dependencies. Furthermore, our approach demonstrates efficiency regarding the pretraining dataset size and the number of epochs required. This method overcomes limitations present in other approaches, including considering various backbone architectures, addressing multiple prediction horizons, reducing reliance on hand-crafted augmentations, and streamlining the pretraining process into a single stage. These findings highlight the potential of our approach in diverse video-based tasks such as activity recognition, motion planning, and scene understanding

Revealing routes of cellular differentiation by single-cell RNA-seq

Differentiation of multipotent stem cells is controlled by the intricate regulatory interactions of thousands of genes. It remains one of the major challenges to understand how nature has designed such robust and reproducible regulatory mechanisms. Knowing the detailed structure of the underlying lineage trees is the basis for investigating the molecular control of this process. The recent availability of large-scale sensitive single-cell RNAseq protocols has enabled the generation of snapshot data covering the entire spectrum of cell states in a systemof interest. Consequently, a large number of computational methods for the reconstruction of cellular differentiation trajectories have been developed. Here, I will provide a detailed overview of the concepts and ideas behind some of these algorithms and discuss the particular aspects addressed by each method

A spatio-temporal learning approach for crowd activity modelling to detect anomalies

With security and surveillance gaining paramount importance in recent years, it has become important to reliably automate some surveillance tasks for monitoring crowded areas. The need to automate this process also supports human operators who are overwhelmed with a large number of security screens to monitor. Crowd events like excess usage throughout the day, sudden peaks in crowd volume, chaotic motion (obvious to spot) all emerge over time which requires constant monitoring in order to be informed of the event build up. To ease this task, the computer vision community has been addressing some surveillance tasks using image processing and machine learning techniques. Currently tasks such as crowd density estimation or people counting, crowd detection and abnormal crowd event detection are being addressed. Most of the work has focused on crowd detection and estimation with the focus slowly shifting on crowd event learning for abnormality detection.This thesis addresses crowd abnormality detection. However, by way of the modelling approach used, implicitly, the tasks of crowd detection and estimation are also handled. The existing approaches in the literature have a number of drawbacks that keep them from being scalable for any public scene. Most pieces of work use simple scene settings where motion occurs wholly in the near-field or far-field of the camera view. Thus, with assumptions on the expected location of person motion, small blobs are arbitrarily filtered out as noise when they may be legitimate motion in the far-field. Such an approach makes it difficult to deal with complex scenes where entry/exit points occur in the centre of the scene or multiple pathways running from the near to the far-field of the camera view that produce blobs of differing sizes. Further, most authors assume the number of directions people motion should exhibit rather than discover what these may be. Approaches with such assumptions would result in loss of accuracy while dealing with (say) a railway platform which shows a number of motion directions, namely two-way, one-way, dispersive, etc. Finally, very few contributions of work use time as a video feature to model the human intuitiveness of time-of-day abnormalities. That is certain motion patterns may be abnormal if they have not been seen for a given time of day. Most works use it (time) as an extra qualifier to spatial data for trajectory definition.In this thesis most of these drawbacks have been addressed by dealing with these in the modelling of crowd activity. Firstly, no assumptions are made on scene structure or blob sizes resulting therefrom. The optical flow algorithm used is robust and even the noise presented (which is infact unwanted motion of swaying hands and legs as opposed to that from the torso) is fairly consistent and therefore can be factored into the modelling. Blobs, no matter what the size are not discarded as they may be legitimate emerging motion in the far-field. The modelling also deals with paths extending from the far to the near-field of the camera view and segments these such that each segment contains self-comparable fields of motion. The need for a normalisation factor for comparisons across near and far field motion fields implies prior knowledge of the scene. As the system is intended for generic public locations having varying scene structures, normalisation is not an option in the processing used and yet the near & far-field motion changes are accounted for. Secondly, this thesis describes a system that learns the true distribution of motion along the detected paths and maintains these. The approach is such that doing so does not generalise the direction distributions which would cause loss in precision. No impositions are made on expected motion and if the underlying motion is well defined (one-way or two-way), then this is represented as a well defined distribution and as a mixture of directions if the underlying motion presents itself as so.Finally, time as a video feature is used to allow for activity to re-enforce itself on a daily basis such that motion patterns for a given time and space begin to define themselves through re-enforcement which acts as the model used for abnormality detection in time and space (spatio-temporal). The system has been tested with real-world data datasets with varying fields of camera view. The testing has shown no false negatives, very few false positives and detects crowd abnormalities quite well with respect to the ground truths of the datasets used