23 May 2013 Object detection and classification using image moment functions in the applied to video and imagery analysis
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Abstract
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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Olegs Mise, Stephen Bento, "Object detection and classification using image moment functions in the applied to video and imagery analysis", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87450V (23 May 2013); doi: 10.1117/12.2015142; https://doi.org/10.1117/12.2015142
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