12 research outputs found
DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
Vision is the richest and most cost-effective technology for Driver
Monitoring Systems (DMS), especially after the recent success of Deep Learning
(DL) methods. The lack of sufficiently large and comprehensive datasets is
currently a bottleneck for the progress of DMS development, crucial for the
transition of automated driving from SAE Level-2 to SAE Level-3. In this paper,
we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which
includes real and simulated driving scenarios: distraction, gaze allocation,
drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth
and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A
comparison with existing similar datasets is included, which shows the DMD is
more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated
by extracting a subset of it, the dBehaviourMD dataset, containing 13
distraction activities, prepared to be used in DL training processes.
Furthermore, we propose a robust and real-time driver behaviour recognition
system targeting a real-world application that can run on cost-efficient
CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated
with different types of fusion strategies, which all reach enhanced accuracy
still providing real-time response.Comment: Accepted to ECCV 2020 workshop - Assistive Computer Vision and
Robotic
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Handling nominal features in anomaly intrusion detection problems
Computer network data stream used in intrusion detection usually involve many data types. A common data type is that of symbolic or nominal features. Whether being coded into numerical values or not, nominal features need to be treated differently from numeric features. This paper studies the effectiveness of two approaches in handling nominal features: a simple coding scheme via the use of indicator variables and a scaling method based on multiple correspondence analysis (MCA). In particular, we apply the techniques with two anomaly detection methods: the principal component classifier (PCC) and the Canberra metric. The experiments with KDD 1999 data demonstrate that MCA works better than the indicator variable approach for both detection methods with the PCC coming much ahead of the Canberra metric
Handling nominal features in anomaly intrusion detection problems
Computer network data stream used in intrusion detection usually involve many data types. A common data type is that of symbolic or nominal features. Whether being coded into numerical values or not, nominal features need to be treated differently from numeric features. This paper studies the effectiveness of two approaches in handling nominal features: a simple coding scheme via the use of indicator variables and a scaling method based on multiple correspondence analysis (MCA). In particular, we apply the techniques with two anomaly detection methods: the principal component classifier (PCC) and the Canberra metric. The experiments with KDD 1999 data demonstrate that MCA works better than the indicator variable approach for both detection methods with the PCC coming much ahead of the Canberra metric