A PCLR-GIST Algorithm for Fast Image Retrieval in Visual Indoor Localization System

Recently, indoor localization technology has attracted the attention of many scholars and IT industry enterprises. The main problem that exists in the current algorithms is time latency and localization accuracy. We propose a fast image retrieval algorithm for vision-based indoor localization system. The proposed algorithm is based on PCA, Linear Regression and GIST when an image database is built. Gist feature of database image is processed as training data set. Through PCA the key information is extracted, then we fit a model by linear regression. We compare the performance of our algorithm with C-GIST algorithm. It will be also demonstrated that our proposed algorithm takes an average of 31.5 percent less time for image retrieval in coarse matching step, and the accuracy is better than C-GIST. The most prominent contribution is the results of our algorithm can be directly applied to the localization of a region of narrower width, and the latency of the algorithm have no relation to the size of the visual database.

[1]  Lin Ma,et al.  Received Signal Strength Recovery in Green WLAN Indoor Positioning System Using Singular Value Thresholding , 2015, Sensors.

[2]  Hongbin Zha,et al.  Coarse-to-fine vision-based localization by indexing scale-Invariant features , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Prof. Satish Bhojannavar,et al.  Face-To-Face Proximity Estimation using Bluetooth on Smartphone , 2016 .

[4]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[5]  Zhaozheng Yin,et al.  Indoor localization with a signal tree , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[6]  Moe Z. Win,et al.  A Machine Learning Approach to Ranging Error Mitigation for UWB Localization , 2012, IEEE Transactions on Communications.

[7]  Avideh Zakhor,et al.  Image Based Localization in Indoor Environments , 2013, 2013 Fourth International Conference on Computing for Geospatial Research and Application.

[8]  Hongbin Zha,et al.  Vision-based Global Localization Using a Visual Vocabulary , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[9]  Lin Ma,et al.  A Fast C-GIST Based Image Retrieval Method for Vision-Based Indoor Localization , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[10]  Shahrokh Valaee,et al.  A weighted KNN epipolar geometry-based approach for vision-based indoor localization using smartphone cameras , 2014, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[11]  Ronald Raulefs,et al.  Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications , 2017, IEEE Communications Surveys & Tutorials.

[12]  Lin Ma,et al.  Visual location recognition using smartphone sensors for indoor environment , 2015, 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST).