Within this work a novel semi-supervised learning technique is introduced based on a simple iterative learning cycle together with learned thresholding techniques and an ensemble decision support system. State-of-the-art model performance and increased training data volume are demonstrated, through the use of unlabelled data when training deeply learned classification models. The methods presented work independently from the model architectures or loss functions, making this approach applicable to a wide range of machine learning and classification tasks. Evaluation of the proposed approach is performed on commonly used datasets when evaluating semi-supervised learning techniques as well as a number of more challenging image classification datasets (CIFAR-100 and a 200 class subset of ImageNet).

Smart video sensors for applications related to surveillance and security are IOT-based as they use Internet for various purposes. Such applications include crowd behaviour monitoring and advanced decision support systems operating and transmitting information over internet. The analysis of crowd and pedestrian behaviour is an important task for smart IoT cameras and in particular video processing. In order to provide related behavioural models, simulation and tracking approaches have been considered in the literature. In both cases ground truth is essential to train deep models and provide a meaningful quantitative evaluation. We propose a framework for crowd simulation and automatic data generation and annotation that supports multiple cameras and multiple targets. The proposed approach is based on synthetically generated human agents, augmented frames and compositing techniques combined with path finding and planning methods. A number of popular crowd and pedestrian data sets were used to validate the model, and scenarios related to annotation and simulation were considered.

Abstract In this work, we present the modular crowd simulation evaluation through composition framework, which provides a quantitative comparison between different pedestrian and crowd simulation approaches. Evaluation is made based on the comparison of source footage against synthetic video created through novel composition techniques. The proposed framework seeks to reduce the complexity of simulation evaluation and provide a platform from which the comparison of differing simulation algorithms and parametric tuning can be conducted to improve simulation accuracy or provide measures of similarity between crowd simulation algorithms and source data. Through the use of features designed to mimic the human visual system, specific simulation properties can be evaluated relative to sample footage. Validation was performed on a number of popular crowd data sets and through comparisons of multiple pedestrian and crowd simulation algorithms.

In this paper we address the notion of risk assessment of three dimensional scenes. Furthermore through the use of local feature recognition techniques and machine learning we perform this analysis on real time point cloud recordings. We provide a definition of risk and potential hazards that incorporates different elements but mainly focuses on intrinsic risk related properties of an object (e.g sharpness). A 3D Voxel HOG descriptor is utilised that aims to classify and recognise the presence of hazardous characteristics and features of objects present in a given scene. Additionally we utilise and extend the 3D Risk Scenes Dataset (3DRS) designed for risk evaluation in scene analysis. The effectiveness of our method is tested on captured point cloud sequences containing hazardous and non hazardous data with a high degree of accuracy across all tested data.

In this paper we evaluate the notion of scene analysis with regard to risk. We consider the problem of evaluating risk and potential hazards in an environment and providing a quantified risk score. A definition of risk is given incorporating two elements, Firstly scene stability, where Newtonian Physics are introduced into the scene analysis process, evaluating object stability within a scene. The effectiveness of which is demonstrated by conducting experiments on several scenes including a variety of stability levels. Secondly the analysis of the intrinsic risk related properties of an object, which is estimated using learning techniques and the utilisation of the 3D Voxel HOG descriptor, analysed against the state-of-the-art descriptors. Finally a new dataset is provided that is designed for scene analysis focusing on risk evaluation.