Robust static and moving object detection via multi-scale attentional mechanisms

Alexander Honda; Yang Chen; Deepak Khosla

doi:10.1117/12.2016017

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.

Citation Download Citation

Alexander Honda, Alexander Honda, Yang Chen, Yang Chen, Deepak Khosla, Deepak Khosla, "Robust static and moving object detection via multi-scale attentional mechanisms", Proc. SPIE 8744, Automatic Target Recognition XXIII, 87440S (20 May 2013); doi: 10.1117/12.2016017; https://doi.org/10.1117/12.2016017

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