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The video demonstrates an accurate, low-latency body tracking approach for VR-based applications using Vive Trackers. Using a HTC Vive headset and Vive Trackers, an immersive VR experience, by animating the motions of the avatar as smoothly, rapidly and as accurately as possible, has been created. The user can see her from the first-person view.

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735 Views

With the increasing use of drones for surveillance and monitoring purposes, there is a growing need for reliable and efficient object detection algorithms that can detect and track objects in aerial images and videos. To develop and test such algorithms,  datasets of aerial videos captured from drones are essential.

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956 Views

BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.

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1502 Views

Multiwinding-Transfomer-based (MTB) DC-DC converter did emerge in the last 25 years as an interesting possibility to connect several energy systems and/or to offer higher power density because of the reduction of transformer core material and reduction of power converter stages.  MTB DC-DC converters can be considered as an interesting compromise between non-modular and a modular DC-DC converter since they are themselves modular in the construction.

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1147 Views

Description of the proposed method is presented with the support of experimental videos.

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For more information please take a look at the corresponding paper (DOI: 10.1109/JBHI.2019.2963786)

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Low light scenes often come with acquisition noise, which not only disturbs the viewers, but it also makes video compression harder. These type of videos are often encountered in cinema as a result of artistic perspective or the nature of a scene. Other examples include shots of wildlife (e.g. mobula rays at night in Blue Planet II), concerts and shows, surveillance camera footage and more. Inspired by all above, we are proposing a challenge on encoding low-light captured videos.

Last Updated On: 
Fri, 05/01/2020 - 09:40

The Data Fusion Contest 2016: Goals and Organization

The 2016 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data.

New multi-source, multi-temporal data including Very High Resolution (VHR) multi-temporal imagery and video from space were released. First, VHR images (DEIMOS-2 standard products) acquired at two different dates, before and after orthorectification:

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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the 

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5073 Views

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