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In a rescue operation walking robots offer a great deal of flexibility in traversing uneven terrain in an uncontrolled environment. For such a rescue robot each motion is a potential vital sign but the existing techniques for motion detection have severe limitations in dealing with strong levels of ego-motion on walking robots. This paper proposes an optical flow based method for the detection of moving objects using a single camera mounted on a hexapod robot for an application in a rescue scenario. The proposed algorithm estimates and compensates ego-motion to allow for object detection while the robot is moving. Our algorithm can deal with strong rotation and translation in 3D, using a first-order-flow motion model, with four degrees of freedom. Two alternative object detection methods using a 2D-histogram based vector clustering and motion compensated frame differencing respectively are examined for the detection of slow and fast moving objects. In addition to a software implementation, the system was implemented on an FPGA, enabling processing in real-time at 31 fps.