Motion Estimation Object Detection Using SAD
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Solution Overview
Problem
Current methods for real-time object detection in videos are computationally intensive due to the need for full object detection in each frame, which is resource-heavy and inefficient, especially when dealing with videos that require processing multiple frames.
Innovation Solution
Implementing a system that performs motion estimation using the sum of absolute difference (SAD) between frames to determine if object detection is necessary, allowing for reduced computation by reusing encoding information from previous frames and applying the classifier function only when significant changes are detected, thereby focusing detection efforts on regions with potential object movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full object detection is performed in each video frame, then object detection accuracy is maintained, but computational cost and processing time increase significantly
Solution Approach 1:
The patent applies motion estimation as a preliminary filtering step before object detection. By calculating motion vectors and identifying regions with significant motion activity in advance, the system determines which regions require full object detection analysis. This preliminary action prevents unnecessary computation in static or low-motion regions while ensuring that all potential object regions are thoroughly analyzed.
2Reliability
If full object detection is performed in each video frame, then detection reliability is maintained, but energy consumption increases
Solution Approach 1:
The patent implements partial object detection by applying the classifier function only to regions identified as having significant motion activity. Instead of performing exhaustive object detection across the entire video frame, the system selectively applies detection resources to regions where objects are likely to appear based on motion estimation results, thereby reducing energy consumption while maintaining detection reliability for moving objects.
3Productivity
If motion estimation is used to reduce computation, then processing efficiency improves, but device complexity increases
Solution Approach 1:
The patent segments the video frame into multiple regions and performs motion estimation and object detection selectively on each region. By dividing the processing task into region-specific operations based on motion activity, the system achieves processing efficiency through targeted analysis while managing complexity through modular, region-based processing units that can be independently implemented.
Data Source
AI summary
Systems and methods of detecting an object using motion estimation may include a processor and motion estimation and object detection logic coupled to the processor. The motion estimation and object detection logic may be configured to include logic to detect an object in a frame of a video based on motion estimation. The video may include a first frame and a second frame. The motion estimation may be performed on a region of the second frame using sum of absolute difference between the region of the second frame and a corresponding region of the first frame.