Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study

Object tracking is an important functionality of edge video analytic systems and services. Multi-object tracking (MOT) detects the moving objects and tracks their locations frame by frame as real scenes are being captured into a video. However, it is well known that real time object tracking on the edge poses critical technical challenges, especially with edge devices of heterogeneous computing resources. This paper examines the performance issues and edge-specific optimization opportunities for object tracking. We will show that even the well trained and optimized MOT model may still suffer from random frame dropping problems when edge devices have insufficient computation resources. We present several edge specific performance optimization strategies, collectively coined as EMO, to speed up the real time object tracking, ranging from window-based optimization to similarity based optimization. Extensive experiments on popular MOT benchmarks demonstrate that our EMO approach is competitive with respect to the representative methods for on-device object tracking techniques in terms of run-time performance and tracking accuracy. EMO is released on Github at https://github.com/git-disl/EMO

Definition Of The Minimal Contents For The Molecular Simulation Of The Yeast Cytoplasm

Funding VK gratefully acknowledges the receipt of a scholarship under the Aberdeen-Curtin Alliance collaborative Ph.D. program. Acknowledgments We thank Prof. Grant Brown (University of Toronto) for making the yeast proteomics datasets available to us.Peer reviewedPublisher PD

What determines sub-diffusive behavior in crowded protein solutions?

This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk), access to which was provided by the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim (https://www.hecbiosim.ac.uk/), supported by EPSRC (grant no. EP/R029407/1). Analysis and visualization of the simulation data were conducted at the Pawsey Supercomputing Centre, therefore this work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia, as well as resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. V.K. gratefully acknowledges the receipt of a scholarship under the Aberdeen-Curtin Alliance collaborative PhD program.Peer reviewedPostprin

Effect of Macromolecular Crowding on the Diffusion of RNAs and RNAprotein Complexes in the Translation Machinery

NO ABSTRAC

VIP: A Python package for high-contrast imaging

International audienc

VIP: A Python package for high-contrast imaging

International audienc