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Proceedings Paper

Robust static and moving object detection via multi-scale attentional mechanisms
Author(s): Alexander Honda; Yang Chen; Deepak Khosla
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Paper Abstract

Real-time detection of objects in video sequences captured from an aerial platforms is a key task for surveillance applications. It is common to perform expensive frame to frame registration as preprocessing to moving object detection in this type of application, and there is no principled approach to the detection of stationary targets.We explore the Spectral Residual algorithm,6 a fast linearithmic run time saliency model which requires no training and has no temporal dependencies, and is capable of detecting proto-objects in a single image. In this paper we describe methods for enhancing the Spectral Residual saliency algorithm to generate candidate object detections from video sequences captured from moving platforms. These object candidates can then be passed to a classification module for further processing. We describe a method that makes the Spectral Residual algorithm more robust to natural variances in color images, and a pyramidal approach to make the processes more robust to objects of varying size. Furthermore we describe a technique for processing the resulting saliency map into a set of tight bounding boxes suitable for extracting image regions for classification. These methods result in a system that is fast, robust, and efficient with reliable performance for low SWaP surveillance platforms.

Paper Details

Date Published: 20 May 2013
PDF: 9 pages
Proc. SPIE 8744, Automatic Target Recognition XXIII, 87440S (20 May 2013); doi: 10.1117/12.2016017
Show Author Affiliations
Alexander Honda, HRL Labs., LLC (United States)
Yang Chen, HRL Labs., LLC (United States)
Deepak Khosla, HRL Labs., LLC (United States)

Published in SPIE Proceedings Vol. 8744:
Automatic Target Recognition XXIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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