1,721,022 research outputs found
Sparse random neural networks for online anomaly detection on sensor nodes
No full textThis research received funding from the Flemish Government, Belgium under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" programme
Sparse random neural networks for online anomaly detection on sensor nodes
No full textWhether it is used for predictive maintenance, intrusion detection or surveillance, on-device anomaly detection is a very valuable functionality in sensor and Internet-of-things (IoT) systems. In this paper, we introduce a novel anomaly detection technique based on sparse, random neural networks. The sparsity in the model allows for a very efficient implementation on embedded or resource constrained hardware. Our approach supports continuous online learning where the model is deployed to the sensor device without any prior training. As new data becomes available, the model is updated and becomes better at detecting anomalies. We experimentally validate our approach on several default benchmark data sets in the visual domain as well as on industrial quality inspection and predictive maintenance tasks. We show that our approach achieves a very favorable trade-off between computational cost and anomaly detection accuracy
Decoupled appearance and motion learning for efficient anomaly detection in surveillance video
No full textAutomating the analysis of surveillance video footage is of great interest when urban environments or industrial sites are monitored by a large number of cameras. As anomalies are often context-specific, it is hard to predefine events of interest and collect labeled training data. A purely unsupervised approach for automated anomaly detection is much more suitable. For every camera, a separate algorithm could then be deployed that learns over time a baseline model of appearance and motion related features of the objects within the camera viewport. Anything that deviates from this baseline is flagged as an anomaly for further analysis downstream. We propose a new neural network architecture that learns the normal behavior in a purely unsupervised fashion. In contrast to previous work, we use latent code predictions as our anomaly metric. We show that this outperforms frame reconstruction-based and prediction-based methods on different benchmark datasets both in terms of accuracy and robustness against changing lighting and weather conditions. By decoupling an appearance and a motion model, our model can also process 16 to 45 times more frames per second than related approaches which makes our model suitable for deploying on the camera itself or on other edge devices
Automated training of location-specific edge models for traffic counting
No full textThis research received funding from the Flemish Government, Belgium under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen'' programme, and from imec, Belgium under the CityFlows AAA programme
Automated training of location-specific edge models for traffic counting
No full textDeep neural networks are the state of the art for various machine learning problems dealing with large amounts of rich sensor data. It is often desirable to evaluate these models on edge devices instead of relying on cloud computing. In this paper, we perform traffic counting using surveillance cameras. Edge computing is required as only aggregated counts should leave the device and not the privacy sensitive video frames. Unfortunately, only small object detection models are suited for edge devices which results in sub-optimal performance. We introduce location specific models that are each trained for one specific camera. The model does not need to generalize to other locations. We show that smaller specialized models can outperform large general purpose models. We propose an automated way to train these small models without human intervention. We experimentally show that we can achieve a similar counting accuracy with 5x fewer parameters than state-of-the-art techniques
Multi-branch neural networks for video anomaly detection in adverse lighting and weather conditions
Get PDFHybrid Edge–Cloud Models for Bearing Failure Detection in a Fleet of Machines
Get PDFReal-time condition monitoring of machinery is increasingly being adopted to minimize costs and enhance operational efficiency. By leveraging large-scale data acquisition and intelligent algorithms, failures can be detected and predicted, thereby reducing machine downtime. In this paper, we present a novel hybrid edge–cloud system for detecting rotational bearing failures using accelerometer data. We evaluate both supervised and unsupervised neural network approaches, highlighting their respective strengths and limitations. Supervised models demonstrate high accuracy but require labeled datasets representative of the failures of interesting data that are challenging to acquire due to the rarity of anomalies. Conversely, unsupervised models rely on data from normal operational conditions, which is more readily available. However, these models classify all deviations from normalcy as anomalies, including those unrelated to failure, leading to costly false positives. To address these challenges, we propose a distributed system that integrates supervised and unsupervised learning. A compact unsupervised model is deployed on edge devices near the machines to compress sensor data, which are then transmitted to a centralized cloud-based system. Over time, these data are automatically labeled and used to train a supervised model, improving the accuracy of failure predictions. Our approach enables efficient, scalable failure detection across a fleet of machines while balancing the trade-offs between supervised and unsupervised learning
Learning Logic Constraints from Demonstration
Get PDFThis research was partially funded by the Flemish Government (Flanders AI Research Program)
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