Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. We propose a scenario that holds immense appeal for various real-world applications, where a model adapts to handle a stream of machines with distribution shifts Tests on real packaging data proved the feasibility of Continual Learning for addressing such problems. Our study uncovers the limitations of previous algorithms in the Domain Incremental Learning. Our research presents a novel approach for tackling multi-label tasks in Continual Learning, achieving superior performance compared to existing approaches found in the literature. Our method not only achieves optimal performance but also has logarithmic complexity, significantly reducing computation times.

Predictive Maintenance technologies are particularly appealing for Industrial Equipment producers, as they pave the way to the selling of high added-value services and customized maintenance plans. However, standard Predictive Maintenance approaches assume the availability of sensor measurements, and the costs associated with adding sensors or remotely accessing sensor readings may discourage the development of such technologies. In this context, Alarm Forecasting can be very useful as it represents a low-cost alternative or helpful support to sensor-based Predictive Maintenance. In this work, we propose a new formulation for the Alarm Forecasting problem, framed as a multi-label classification task. We present a novel deep learning-based approach called FORMULA (alarm FORecasting in MUlti-LAbel setting). FORMULA leverages Transformer, a popular Neural Network architecture in the field of Natural Language Processing. To cope with alarm imbalance, we draw inspiration from Segmentation and Object Detection. Thus, FORMULA is trained by minimizing the Weighted Focal Loss, which turns out to be very effective in predicting rare alarms. These alarms, even if they are difficult to predict by nature, often are business-critical. We assess the proposed approach on a representative real-world problem from the packaging industry. In particular, we show that it outperforms not only classic multilabel techniques but also models based on recurrent neural networks. As regards the latter, the proposed approach also exhibits a lower computational burden, both in terms of training time and model size. To foster research in the field and reproducibility, we also publicly share the alarm logs dataset and the code used to perform the experiments.Note to Practitioners—This paper was motivated by the problem of monitoring equipment in the scenario of dairy products packaging, under the mild assumption that logs of the alarm generated by the packaging machines are available. This paper proposes an alarm forecasting algorithm. Its goal is to predict if any alarm will occur in the future, based only on past alarm logs. The limits of the considered future window can be defined arbitrarily, so there is enough time to perform corrective actions. Thus, the proposed approach aims to prevent unexpected downtime that would not only hinder productivity but also imply significant material waste. The proposed approach leverages methodologies from Natural Language Processing and Object Detection to deal with rare alarms that are often very informative in the industrial scenario. Besides, both the code and the real-world industrial datasets used to evaluate the methodology are available publicly. Currently, the proposed approach only uses alarm logs. Especially in the context of Industry 4.0, where many sensory data may be available, this is a limitation. Thus, the described approach might be extended by integrating alarm logs with sensory data. This integration is expected to improve the estimation of equipment health state. The results described in this paper may find application not only in the manufacturing sector but also in different areas such as Cyber Security, where log files keep activity records of each process performed.

Abstract In human behaviour analysis, the visual focus of attention (VFOA) of a person is a very important cue. VFOA detection is difficult, though, especially in a unconstrained and crowded environment, typical of video surveillance scenarios. In this paper, we estimate the VFOA by defining the Subjective View Frustum, which approximates the visual field of a person in a three‐dimensional representation of the scene. This opens up to several intriguing behavioural investigations. In particular, we propose the Inter‐Relation Pattern Matrix, which suggests possible social interactions between the people present in a scene. Theoretical justifications and experimental results substantiate the validity and the goodness of the analysis performed.

In surveillance applications, head and body orientation of people is of primary importance for assessing many behavioral traits. Unfortunately, in this context people are often encoded by a few, noisy pixels so that their characterization is difficult. We face this issue, proposing a computational framework which is based on an expressive descriptor, the covariance of features. Covariances have been employed for pedestrian detection purposes, actually a binary classification problem on Riemannian manifolds. In this paper, we show how to extend to the multiclassification case, presenting a novel descriptor, named weighted array of covariances, especially suited for dealing with tiny image representations. The extension requires a novel differential geometry approach in which covariances are projected on a unique tangent space where standard machine learning techniques can be applied. In particular, we adopt the Campbell-Baker-Hausdorff expansion as a means to approximate on the tangent space the genuine (geodesic) distances on the manifold in a very efficient way. We test our methodology on multiple benchmark datasets, and also propose new testing sets, getting convincing results in all the cases.

We present a novel approach for detecting social interactions in a crowded scene by employing solely visual cues. The detection of social interactions in unconstrained scenarios is a valuable and important task, especially for surveillance purposes. Our proposal is inspired by the social signaling literature, and in particular it considers the sociological notion of F-formation. An F-formation is a set of possible configurations in space that people may assume while participating in a social interaction. Our system takes as input the positions of the people in a scene and their (head) orientations; then, employing a voting strategy based on the Hough transform, it recognizes F-formations and the individuals associated with them. Experiments on simulations and real data promote our idea.

Pedestrian detection is a crucial task in several video surveillance and automotive scenarios, but only a few detection systems are designed to be realized on an embedded architecture, allowing to increase the processing speed which is one of the key requirements in real applications. In this paper, we propose a novel SoC (System on Chip) architecture for fast pedestrian detection in video. Our implementation is based on a linear SVM (Support Vector Machine) classification frame- work, learned on a set of overlapped image patches. Each patch is described by a covariance matrix of a set of image features. Exploiting the inner parallelism of the FPGA (Field Programmable Gate Array) boards, we dramatically accelerate the covariance matrices computation that plays a crucial role in the framework. In the experiments, we show the effectiveness and the efficiency of our pedestrian detection system, reaching a detection speed of 132 fps at VGA resolution.

In this paper we propose a novel part-based framework for pedestrian detection. We model a human as a hierarchy of fixed overlapped parts, each of which described by covariances of features. Each part is modeled by a boosted classifier, learnt using Logitboost on Riemannian manifolds. All the classifiers are then linked to form a high-level classifier, through weighted summation, whose weights are estimated during the learning. The final classifier is simple, light and robust. The experimental results show that we outperform the state-of-the-art human detection performances on the INRIA person dataset.

Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection performances of the state-of-the-art approach of Tuzel et al. can be greatly improved, from both a computational and a qualitative point of view, by considering practical and theoretical issues, and allowing also the estimation of occlusions in a fine way. The resulting detection system reaches the best performance on the INRIA dataset, setting novel state-of-the art results.