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.
 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).
 P. Angelov. “Anomalous system state identification”. GB1208542.9, May 2012.
 P. Angelov, Autonomous Learning Systems: From Data Streams to Knowledge in Real Time, John Wiley and Sons, 2012.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.