The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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In this paper, we present a spatial-temporal attention-aware learning (STAL) method for video-based person re-identification. Most existing person re-identification methods aggregate image features identically to represent persons, which are extracted from the same receptive field across video frames. However, the image quality may be varying for different spatial regions and changing over time, which shall contribute to person representation and matching adaptively. Our STAL method aims to attend to the salient parts of persons in videos jointly in both spatial and temporal domains. To achieve this, we slice the video into multiple spatial-temporal units which preserve the body structure of a person and develop a joint spatial-temporal attention model to learn the quality scores of these units. We evaluate the proposed method on three challenging datasets including iLIDS-VID, PRID-2011, and the large-scale MARS dataset, and consistently improve the rank-1 accuracy by a large margin of 5.7%, 0.9%, and 6.6% respectively, in comparison with the state-of-the-art methods.
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