9,989 research outputs found
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
In this work, we propose a novel Spatial-Temporal Attention (STA) approach to
tackle the large-scale person re-identification task in videos. Different from
the most existing methods, which simply compute representations of video clips
using frame-level aggregation (e.g. average pooling), the proposed STA adopts a
more effective way for producing robust clip-level feature representation.
Concretely, our STA fully exploits those discriminative parts of one target
person in both spatial and temporal dimensions, which results in a 2-D
attention score matrix via inter-frame regularization to measure the
importances of spatial parts across different frames. Thus, a more robust
clip-level feature representation can be generated according to a weighted sum
operation guided by the mined 2-D attention score matrix. In this way, the
challenging cases for video-based person re-identification such as pose
variation and partial occlusion can be well tackled by the STA. We conduct
extensive experiments on two large-scale benchmarks, i.e. MARS and
DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which
significantly outperforms the state-of-the-arts with a large margin of more
than 11.6%.Comment: Accepted as a conference paper at AAAI 201
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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