Abstract: A plurality of remote sensing images of a scene are received. A potential target object can be identified in one of the images, wherein the target object has a low signal-to-noise ratio (SNR). A candidate motion path of the target object can be generated based upon the images. A predicted position of the target object along the candidate motion path is determined for each of the remote sensing images. An image chip is extracted from each of the images, where each image chip is centered about the predicted position of the target object in its corresponding image. A sum image chip is generated based upon the image chips. An indication that the potential target object is an actual object in the images is output based upon a value of a center pixel of the sum image chip.

Abstract: Methods and apparatuses for identifying a track for a target are disclosed herein. In a general embodiment, trees for targets are identified. Each tree in the trees includes observations that are connected to each other by branches and each observation has a score. A selected track in a tree is identified within the trees with an observation having a highest score. A group of branches is added to the selected track from other trees other than the tree having the selected track to form the track for a target, enabling reducing processing resources used to identify the track.

Abstract: A method and apparatus for analyzing a sequence of images. Signal-to-noise ratios are identified for potential tracks using a tree having hierarchies of nodes identifying potential positions for an object over a period of time and using the sequence of images. Each hierarchy in the hierarchies of nodes represents a time and the potential positions in the tree form the potential tracks for the object. A potential track is selected from the potential tracks as an established track for the object using the signal-to-noise ratios, and a detection of the object is reported.

Abstract: Methods and apparatuses for analyzing a sequence of images for an object are disclosed herein. In a general embodiment, the method identifies a region of interest in the sequence of images. The object is likely to move within the region of interest. The method divides the region of interest in the sequence of images into sections and calculates signal-to-noise ratios for a section in the sections. A signal-to-noise ratio for the section is calculated using the section in the image, a prior section in a prior image to the image, and a subsequent section in a subsequent image to the image. The signal-to-noise ratios are for potential velocities of the object in the section. The method also selects a velocity from the potential velocities for the object in the section using a potential velocity in the potential velocities having a highest signal-to-noise ratio in the signal-to-noise ratios.

Abstract: Methods and apparatuses for analyzing a sequence of images for an object are disclosed herein. In a general embodiment, the method identifies a region of interest in the sequence of images. The object is likely to move within the region of interest. The method divides the region of interest in the sequence of images into sections and calculates signal-to-noise ratios for a section in the sections. A signal-to-noise ratio for the section is calculated using the section in the image, a prior section in a prior image to the image, and a subsequent section in a subsequent image to the image. The signal-to-noise ratios are for potential velocities of the object in the section. The method also selects a velocity from the potential velocities for the object in the section using a potential velocity in the potential velocities having a highest signal-to-noise ratio in the signal-to-noise ratios.

Abstract: Methods and apparatuses for identifying a track for a target are disclosed herein. In a general embodiment, trees for targets are identified. Each tree in the trees includes observations that are connected to each other by branches and each observation has a score. A selected track in a tree is identified within the trees with an observation having a highest score. A group of branches is added to the selected track from other trees other than the tree having the selected track to form the track for a target, enabling reducing processing resources used to identify the track.

Abstract: A technique for detecting changes in a scene perceived by a staring sensor is disclosed. The technique includes acquiring a reference image frame and a current image frame of a scene with the staring sensor. A raw difference frame is generated based upon differences between the reference image frame and the current image frame. Pixel error estimates are generated for each pixel in the raw difference frame based at least in part upon spatial error estimates related to spatial intensity gradients in the scene. The pixel error estimates are used to mitigate effects of camera jitter in the scene between the current image frame and the reference image frame.