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This paper presents the design, implementation and evaluation of a new smartphone application that is capable of real-time object detection using both stationary and moving cameras for embedded systems, particularly, the Android smartphone platform. A new object detection approach, Optical ORB, is presented which is capable of real-time performance at high definition resolutions on a smartphone. In addition, the developed smartphone application has the ability to connect to a remote server and wirelessly send image frames when moving objects appear in the camera’s field of view; thus, allowing the human operator to only view video frames that are of interest. Evaluation experiments show a capability of achieving real-time performance for high definition (HD) resolution video.
Słowa kluczowe
Rocznik
Tom
Strony
15--19
Opis fizyczny
Bibliogr. 18 poz., rys.
Twórcy
autor
- Intelligent Systems Lab, Data Science Group, Lancaster University, UK, LA1 4WA
autor
- Intelligent Systems Lab, Data Science Group, Lancaster University, UK, LA1 4WA
autor
- Intelligent Systems Lab, Data Science Group, Lancaster University, UK, LA1 4WA
autor
- Intelligent Systems Lab, Data Science Group, Lancaster University, UK, LA1 4WA
Bibliografia
- [1] P. Angelov and A. Wilding, “RTSDE: Recursive Total-Sum-Distances-based Density Estimation Approach and its Application forAutonomous eal-Time Video Analytics”, Symposium Series on Computational Intelligence (SSCI ’14; to appear).
- [2] P. Angelov. “Anomalous system state identification”. GB1208542.9, May 2012.
- [3] P. Angelov, Autonomous Learning Systems: From Data Streams to Knowledge in Real Time, John Wiley and Sons, 2012.
- [4] P. Angelov, P. Sadeghi-Tehran, and R. Ramezani, “A Real-time Approach to Autonomous Novelty Detection and Object Tracking in Video Stream”, International Journal of Intelligent Systems, vol. 26, 2011, pp. 189–205.
- [5] R. D. Baruah and P. Angelov, “Evolving Local Means Method for Clustering of Streaming Data”, IEEE World Congress on Computational Intelligence, 2012, pp. 2161–2168.
- [6] J. Y. Bouguet, “Pyramidal implementation of the aftine Lucas Kanade feature tracker description of the algorithm”, Intel Corporation, Microprocessor Research Labs, 1999.
- [7] Y. Cheng, “Mean shift, mode seeking, and clustering”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, 1995, pp. 790–799.
- [8] C. Clarke. “Smartphone Application for Realtime Object Detection”. Master’s thesis, School of Computing and Communications, Lancaster University, 2014.
- [9] I. Cohen and G. Medioni, “Detecting and tracking moving objects for video surveillance”. In: Computer Vision and Pattern Recognition, 1999.IEEE Computer Society Conference on, 1999, pp. 319–325.
- [10] M. Cristani, M. Farenzena, D. Bloisi, and V. Murino, “Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review”, Journal on Advances in Signal Processing, 2010, pp. 1–24.
- [11] C. Harris and M. Stephens, “A combined corner and edge detector.”, Alvey vision conference, 1988.
- [12] K. Jeong and H. Moon, “Object Detection Using FAST Corner Detector Based on Smartphone Platforms”. In: Computers, Networks, Systems and Industrial Engineering (CNSI), 2011 First ACIS/JNU International Conference on, 2011, pp. 111–115.
- [13] K. Kadar, F. de Sorbier, and H. Saito, “Displayed Object Recognition for Smartphone Interaction”, International Conference on Machine Vision Applications, 2013, pp. 20–23.
- [14] K. Matusiak, P. Skulimowski, and P. Strumillo, “Object recognition in a mobile phone application for visually impaired users”. In: Human System Interaction (HSI), 2013 The 6th International Conference on, 2013, pp. 479–484.
- [15] G. Morris and P. Angelov, “Real-time Novelty Detection in Video using Background Subtraction Techniques: State of the art”. In: IEEE International Conference on Systems, Man and Cybernetics (SMC ’14) (to appear).
- [16] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF”. In: Computer Vision (ICCV), 2011 IEEE International Conference on, 2011, pp. 2564–2571.
- [17] P. Sadeghi-Tehran, C. Clarke, and P. Angelov, “A Real-time Approach for Autonomous Detection and Tracking of Moving Objects from UAV”. In: IEEE Symposium Series on Computational Intelligence SSCI’14 ; to appear).
- [18] T. Senst, V. Eiselein, and T. Sikora, “Robust Local Optical Flow for Feature Tracking”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 9, 2012, pp. 1377–1387.
Typ dokumentu
Bibliografia
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