Incorporating Geo-Tagged Mobile Videos Into Context-Aware Augmented Reality Applications

In recent years, augmented reality (AR) is gaining much attention from  the research community and industry. With AR, users look at the real-world space through an AR browser where the content is superimposed on the physical world as objects. AR is even regarded as the next-generation of web browser. However, there are challenges with popularizing AR usage. There is not enough AR content because creating the content is not only time-consuming but also  expertise-required. Thus, by leveraging the availability of big user-generated mobile content, we propose to incorporate geo-tagged mobile videos into AR applications. With our framework, any user can generate AR contents.

To enhance user’s experience, we focus on context-aware AR solution using rich-censored data including location (from GPS) and direction (compass). We propose filtering algorithms to effectively select a set of most interesting video segments out of a large video dataset so that the selected scenes can be automatically retrieved and displayed in AR applications. For the filtering, we define an interesting video segment as a sequence of video frames that follow a particular pattern (borrowed from film studies), including tracking, panning, zooming, and arching scenes.

We developed a demo regarding the integration of AR and geo-tagged user-generated mobile videos, conducted experiments to find interesting video segments from a large collection of videos, which mostly contains non-interesting content.

Source code


Hien To, Hyerim Park, Seon Ho Kim, and Cyrus Shahabi, Incorporating Geo-Tagged Mobile Videos Into Context-Aware Augmented Reality Applications, The Second IEEE International Conference on Multimedia Big Data (IEEE BigMM 2016), Taipei, Taiwan, April 20-22, 2016

SCAWG: A Toolbox for Generating Synthetic Workload for Spatial Crowdsourcing

Existing studies in mobile crowdsourcing (aka spatial crowdsourcing), a hot research area in recent years, face the problem of lacking real­world datasets. We thus published a synthetic dataset generator for producing common datasets for mobile crowdsourcing.

The toolbox can generate synthetic workload patterns based on the spatial (location) and temporal (time) distributions of workers and tasks. As shown in the figure below, it also takes into account the various real-world constraints, such as worker region and worker capacity, worker activeness and temporal workload.


Link to the toolbox


Hien To, Mohammad Asghari, Dingxiong Deng, and Cyrus Shahabi, SCAWG: A Toolbox for Generating Synthetic Workload for Spatial Crowdsourcing, In Proceeding of International Workshop on Benchmarks for Ubiquitous Crowdsourcing: Metrics, Methodologies, and Datasets (CROWDBENCH 2016), Sydney, Australia, March 14-18, 2016