Detecting Moving Objects Using a Novel Optical-Flow-Based
  Range-Independent Invariant
Daniel Raviv Juan D. Yepes Ayush Gowda
This paper focuses on a novel approach for detecting moving objects during
camera motion. We present an optical-flow-based transformation that yields a
consistent 2D invariant image output regardless of time instants, range of
points in 3D, and the speed of the camera. In other words, this transformation
generates a lookup image that remains invariant despite the changing projection
of the 3D scene and camera motion. In the new domain, projections of 3D points
that deviate from the values of the predefined lookup image can be clearly
identified as moving relative to the stationary 3D environment, making them
seamlessly detectable. The method does not require prior knowledge of the
direction of motion or speed of the camera, nor does it necessitate 3D point
range information. It is well-suited for real-time parallel processing,
rendering it highly practical for implementation. We have validated the
effectiveness 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 ways
for moving objects detection during camera motion, and also lays the foundation
for future research in the context of moving object detection during
six-degrees-of-freedom camera motion.