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This paper focuses on a novel approach for detecting moving objects duringcamera motion. We present an optical-flow-based transformation that yields aconsistent 2D invariant image output regardless of time instants, range ofpoints in 3D, and the speed of the camera. In other words, this transformationgenerates a lookup image that remains invariant despite the changing projectionof the 3D scene and camera motion. In the new domain, projections of 3D pointsthat deviate from the values of the predefined lookup image can be clearlyidentified as moving relative to the stationary 3D environment, making themseamlessly detectable. The method does not require prior knowledge of thedirection of motion or speed of the camera, nor does it necessitate 3D pointrange information. It is well-suited for real-time parallel processing,rendering it highly practical for implementation. We have validated theeffectiveness of the new domain through simulations and experiments,demonstrating its robustness in scenarios involving rectilinear camera motion,both in simulations and with real-world data. This approach introduces new waysfor moving objects detection during camera motion, and also lays the foundationfor future research in the context of moving object detection duringsix-degrees-of-freedom camera motion.

Detecting Moving Objects Using a Novel Optical-Flow-Based Range-Independent Invariant