Social conflicts appearing in the media are increasing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a powerful assistance to public and private security agents. Since the arrival of deep learning, object detection and classification systems have experienced a large improvement in both accuracy and versatility. However, deep learning-based object detection and classification systems often require expensive GPU-based hardware to work properly. This paper presents a novel deep learning-based foreground anomalous object detection system for video streams supplied by panoramic cameras, specially designed to build power efficient video surveillance systems. The system optimises the process of searching for anomalous objects through a new potential detection generator managed by three different multivariant homoscedastic distributions. Experimental results obtained after its deployment in a Jetson TX2 board attest the good performance of the system, postulating it as a solvent approach to power saving video surveillance systems.

Shared control is a strategy used in assistive platforms to combine human and robot orders to achieve a goal. Collaborative control is a specific shared control approach, in which user's and robot's commands are merged into an emergent one in a continuous way. Robot commands tend to improve efficiency and safety. However, sometimes, assistance can be rejected by users when their commands are too altered. This provokes frustration and stress and, usually, decreases emergent efficiency. To improve acceptance, robot navigation algorithms can be adapted to mimic human behavior when possible. We propose a novel variation of the well-known dynamic window approach (DWA) that we call biomimetical DWA (BDWA). The BDWA relies on a reward function extracted from real traces from volunteers presenting different motor disabilities navigating in a hospital environment using a rollator for support. We have compared the BDWA with other reactive algorithms in terms of similarity to paths completed by people with disabilities using a robotic rollator in a rehabilitation hospital unit. The BDWA outperforms all tested algorithms in terms of likeness to human paths and success rate.

Classification trees are a powerful tool for mining non-stationary data streams. In these situations, mas sive data are constantly generated at high speed and the underlying target function can change over time. The iadem family of algorithms is based on Hoeffding’s and Chernoff’s bounds and induces online deci sion trees from data streams, but is not able to handle concept drift. This study extends this family to deal with time-changing data streams. The new online algorithm, named iadem-3, performs two main actions in response to a concept drift. Firstly, it resets the variables affected by the change and main tains unbroken the structure of the tree, which allows for changes in which consecutive target functions are very similar. Secondly, it creates alternative models that replace parts of the main tree when they significantly improve the accuracy of the model, thereby rebuilding the main tree if needed. An online change detector and a non-parametric statistical test based on Hoeffding’s bounds are used to guaran tee this significance. A new pruning method is also incorporated in iadem-3, making sure that all split tests previously installed in decision nodes are useful. The learning model is also viewed as an ensem ble of classifiers, and predictions of the main and alternative models are combined to classify unlabeled examples. iadem-3 is empirically compared with various well-known decision tree induction algorithms for concept drift detection. We empirically show that our new algorithm often reaches higher levels of accuracy with smaller decision tree models, maintaining the processing time bounded, irrespective of the number of instances processed.

Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change overtime, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naive Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.

Two extensive research areas in Machine Learning are classification and prediction. Many approaches have been focused in the induction of ensemble to increase learning accuracy of individual classifiers. Recently, new approaches, different to those that look for accurate and diverse base classifiers, are emerging. In this paper we present a system made up of two layers: in the first layer, one ensemble classifier process every example and tries to classify them; in the second layer, one individual classifier is induced using the examples that are not unanimously classified by the ensemble. In addition, the examples that reach to the second layer incorporate new information added in the ensemble. Thus, we can achieve some improvement in the accuracy level, because the second layer can do more informed classifications. In the experimental section we present some results that suggest that our proposal can actually improve the accuracy of the system.