31,820 research outputs found
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.Comment: Oral paper in BMVC 201
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
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