Anomaly detection in video surveillance is challenging due to the variety of anomaly types and definitions, which limit the use of supervised techniques. As such, auto-encoder structures, a type of classical unsupervised method, have recently been utilized in this field. These structures consist of an encoder followed by a decoder and are typically adopted to restructure a current input frame or predict a future frame. However, regardless of whether a 2D or 3D autoencoder structure is adopted, only single-scale information from the previous layer is typically used in the decoding process. This can result in a loss of detail that could potentially be used to predict or reconstruct video frames. As such, this study proposes a novel spatio-temporal U-Net for frame prediction using normal events and abnormality detection using prediction error. This framework combines the benefits of U-Nets in representing spatial information with the capabilities of ConvLSTM for modeling temporal motion data. In addition, we propose a new regular score function, consisting of a prediction error for not only the current frame but also future frames, to further improve the accuracy of anomaly detection. Extensive experiments on common anomaly datasets, including UCSD (98 video clips in total) and CUHK Avenue (30 video clips in total), validated the performance of the proposed technique and we achieved 96.5% AUC for the Ped2 dataset, which is much better than existing autoencoder-based and U-Net-based methods.
The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirements.
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