Location based services are proving to be the next driving factors for growth in smartphones. While GPS solves the problem of accurate localization in outdoor environments, indoor localization is still an area of active research. Emergence of new generation smartphones with low cost sensors, have provided an effective way of indoor localization by pedestrian dead reckoning (PDR). We propose a robust mechanism for detecting the step of a person and estimating his step length. Our system is independent of the location and orientation of the device. Our system is shown to perform 45% better than the traditional PDR systems proposed in prior-art. Another important problem in PDR system is determining the orientation of the mobile device and the direction of user motion. Many systems assume the device to be oriented in the direction of the user motion. Some of the recent systems use accelerometer, magnetometer patterns and PCA to detect the direction of user orientation. We propose a system which uses map matching and particle filtering to determine the direction of user motion. We tabulate our findings on the feasibility of such a system.

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In this paper we propose a crowd sourced approach for solving large scale object retrieval. We have built a tablet application which displays a query image and a database image. The crowd provides their input to indicate, if there is a match between the query and database image or not. We test our application on a crowd of low-income individuals. We observe that our target crowd had a very high accuracy on the considered dataset. We observe significant improvement as compared to vision based image matching algorithms available in prior-art. We also observe that with simplistic interfaces, even low literacy and low income people could participate in the crowdsourcing tasks. This provides them a significant income opportunity. We have validated our claims on two publicly available University of Kentucky datasets and ORL Face recognition dataset.