An object detection apparatus for detecting objects based on visual images captured by a self-moving unit, the object detection apparatus comprising: a sequential images output section (14; 114) for making a train of a first input image and a second input image sequential to the first input image and outputting said train; a local area image processor (16; 116) for calculating local flows, which are optical flows, based on said first input image and said second input image, by applying Gabor filters to each local area in the input image; an inertia information acquiring section (20; 120) for measuring self-motion of the unit to calculate inertia information thereof; a global area image processor (18; 118) for using said inertia information to estimate global flow, which is a motion field of the entire view associated to the self-motion, to create a predictive image of said second input image by warp-converting said first input image based on the shift of each pixel calculated from said global flow using a warp estimation method and for calculating differential image data, said differential image data being a difference between said predictive image and said second input image; a figure-ground segregation section (22; 122) for using said differential image data to refine said local flows, comparing the refined local flows with a predetermined threshold value to extract a figure candidate area, said figure candidate area being the area having a high probability of an object existing in the input image; and an object presence/absence determination section (24; 124) for determining presence/absence of objects in said figure candidate area by performing clustering method of said figure candidate area, wherein said object presence/absence determination section (24; 124) is adapted to determine that the object exists in the image when some figure candidate area still remains. a sequential images output section (14; 114) for making a train of a first input image and a second input image sequential to the first input image and outputting said train; a local area image processor (16; 116) for calculating local flows, which are optical flows, based on said first input image and said second input image, by applying Gabor filters to each local area in the input image; an inertia information acquiring section (20; 120) for measuring self-motion of the unit to calculate inertia information thereof; a global area image processor (18; 118) for using said inertia information to estimate global flow, which is a motion field of the entire view associated to the self-motion, to create a predictive image of said second input image by warp-converting said first input image based on the shift of each pixel calculated from said global flow using a warp estimation method and for calculating differential image data, said differential image data being a difference between said predictive image and said second input image; a figure-ground segregation section (22; 122) for using said differential image data to refine said local flows, comparing the refined local flows with a predetermined threshold value to extract a figure candidate area, said figure candidate area being the area having a high probability of an object existing in the input image; and an object presence/absence determination section (24; 124) for determining presence/absence of objects in said figure candidate area by performing clustering method of said figure candidate area, wherein said object presence/absence determination section (24; 124) is adapted to determine that the object exists in the image when some figure candidate area still remains.