210,885 research outputs found
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation in frames or short clips, followed by lightweight tracking to
generate keypoint predictions linked over the entire video. For frame-level
pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D
extension of this model, which leverages temporal information over small clips
to generate more robust frame predictions. We conduct extensive ablative
experiments on the newly released multi-person video pose estimation benchmark,
PoseTrack, to validate various design choices of our model. Our approach
achieves an accuracy of 55.2% on the validation and 51.8% on the test set using
the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art
performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint
tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack
and webpage: https://rohitgirdhar.github.io/DetectAndTrack
OS Scheduling Algorithms for Memory Intensive Workloads in Multi-socket Multi-core servers
Major chip manufacturers have all introduced multicore microprocessors.
Multi-socket systems built from these processors are routinely used for running
various server applications. Depending on the application that is run on the
system, remote memory accesses can impact overall performance. This paper
presents a new operating system (OS) scheduling optimization to reduce the
impact of such remote memory accesses. By observing the pattern of local and
remote DRAM accesses for every thread in each scheduling quantum and applying
different algorithms, we come up with a new schedule of threads for the next
quantum. This new schedule potentially cuts down remote DRAM accesses for the
next scheduling quantum and improves overall performance. We present three such
new algorithms of varying complexity followed by an algorithm which is an
adaptation of Hungarian algorithm. We used three different synthetic workloads
to evaluate the algorithm. We also performed sensitivity analysis with respect
to varying DRAM latency. We show that these algorithms can cut down DRAM access
latency by up to 55% depending on the algorithm used. The benefit gained from
the algorithms is dependent upon their complexity. In general higher the
complexity higher is the benefit. Hungarian algorithm results in an optimal
solution. We find that two out of four algorithms provide a good trade-off
between performance and complexity for the workloads we studied
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