This paper proposes an object detection algorithm and a framework based on a combination of Normalized Central
Moment Invariant (NCMI) and Normalized Geometric Radial Moment (NGRM). The developed framework allows
detecting objects with offline pre-loaded signatures and/or using the tracker data in order to create an online object
signature representation. The framework has been successfully applied to the target detection and has demonstrated its
performance on real video and imagery scenes.
In order to overcome the implementation constraints of the low-powered hardware, the developed framework uses a
combination of image moment functions and utilizes a multi-layer neural network. The developed framework has been
shown to be robust to false alarms on non-target objects. In addition, optimization for fast calculation of the image
moments descriptors is discussed. This paper presents an overview of the developed framework and demonstrates its
performance on real video and imagery scenes.
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