UAV Video Motion Vector Estimation Using Motion Dynamics
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Solution Overview
Problem
Existing motion estimation (ME) algorithms for unmanned aerial vehicles (UAVs) and video-enabled devices in motion are computationally intensive, leading to latency and reduced efficiency in real-time video processing, particularly in applications requiring high accuracy and low latency.
Innovation Solution
The Motion Dynamics Input Search (MDIS) algorithm optimizes ME by leveraging user input from the controller to guide the search for motion vectors, reducing unnecessary search locations based on the vehicle's motion dynamics, using a modified diamond search pattern that adapts to the drone's movement type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional motion estimation algorithms (FS, DS, HS, TZS) are used, then motion vectors can be calculated, but computational complexity is high leading to latency in real-time video processing
Solution Approach 1:
The patent applies preliminary action by using IMU sensor data to predict motion vectors before the actual video frame processing. The inertial measurement unit continuously tracks device motion, and this pre-acquired motion information is used to generate initial motion vector estimates, eliminating the need for exhaustive search algorithms and significantly reducing encoding latency while maintaining accuracy
Solution Approach 2:
The patent introduces an intermediary approach by using IMU sensor data as a mediator between the physical device motion and the video encoding process. Instead of directly analyzing video frames to estimate motion, the system uses the IMU as an intermediate sensor that captures motion dynamics, which then informs the motion estimation algorithm, reducing computational burden while preserving motion accuracy
2Measurement precision
If exhaustive search algorithms are used to maintain accuracy, then motion estimation precision is high, but computational complexity increases leading to slower processing
Solution Approach 1:
The patent replaces the mechanical video-frame-based motion estimation system with a sensor-based measurement system. Instead of using complex image processing algorithms to detect motion between frames, the system substitutes this with direct motion sensing using IMU accelerometers and gyroscopes, which provide accurate motion data with minimal computational overhead
Solution Approach 2:
The patent enables self-service by allowing the IMU sensor to autonomously provide motion information without requiring complex external processing. The inertial measurement unit independently tracks device motion and directly feeds this information to the motion estimation module, eliminating the need for computationally intensive frame-by-frame analysis while maintaining high accuracy
Data Source
AI summary
In short, the disclosed method incorporates a novel algorithm for optimizing Motion Estimation (ME) for the compression of video originating from cameras mounted on remotely controlled vehicles (RCVs), and devices, with a particular focus on unmanned aerial vehicles (UAV). The basic embodiment enabled by the present invention leverages information about vehicle motion dynamics estimated by the controller/estimator block of the vehicle's control system to estimate the motion of the vehicle between two successive frames and use that information to more efficiently determine the Motion Vectors (MV) for each block in a frame. These and other embodiments are described in more detail in the description.