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This work presents an approach to detect moving objects from Unmanned Aerial Vehicles (UAV). A common framework for most of the existing techniques is using image registration to warp consecutive frames as an ego-motion compensation step and applying frame differencing to detect the moving objects. Assuming a planar scene, we propose the exploitation of telemetry information available from Global Positioning and Inertial Navigation Systems (GPS/INS) to estimate a similarity transformation matrix that would map the image points from one frame to another. In this work, we show that the telemetry-based image registration combined with global registration methods produces more accurate results than the traditional image registration techniques in case of a scene with poor or no texture. To segment the moving objects, we employ the probabilistic background modelling method with mixture of Gaussian distributions.

A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data.

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