Video Semantic Segmentation Evaluation With Ground-Truth Filtering
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Solution Overview
Problem
Existing semantic segmentation methods for video frames in automated vehicles and robots lack temporal consistency, leading to inaccurate evaluations and perverse incentives during training, especially when considering all pixels without semantic relevance.
Innovation Solution
Limit the evaluation of temporal consistency to pixels or parts with ground truth consistency, using a binary mask and matrix operations to efficiently assess the performance of the machine learning system, and weight evaluations based on the cardinality of the ground truth consistency set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temporal consistency is evaluated for all pixels in segmentation frames, then the evaluation process is simple and comprehensive, but it includes semantically meaningless regions that lead to inaccurate evaluations and perverse incentives
Solution Approach 1:
The patent segments the evaluation process into two distinct phases: first identifying semantically meaningful regions through ground truth consistency checking, then performing temporal consistency evaluation only on those segmented regions. This segmentation eliminates meaningless pixels from the evaluation while maintaining focus on relevant semantic content.
Solution Approach 2:
The patent applies different evaluation criteria to different regions of the segmentation frame. By first determining which pixels have ground truth consistency (semantically meaningful regions) and then applying temporal consistency evaluation selectively to those regions, the method ensures that evaluation quality is optimized for semantically relevant areas while ignoring meaningless regions.
2Reliability
If all pixels are included in temporal consistency evaluation, then the evaluation covers the entire frame, but it creates perverse incentives for the machine learning system to output homogeneous segmentation
Solution Approach 1:
The patent extracts and removes semantically meaningless pixels from the evaluation process by first identifying regions with ground truth consistency. This extraction prevents meaningless pixels from influencing the evaluation, thereby eliminating the incentive for the system to output homogeneous segmentation and ensuring evaluation accuracy reflects true semantic understanding.
Solution Approach 2:
The patent implements a feedback mechanism where ground truth consistency information is used to guide the temporal consistency evaluation. By using the ground truth segmentation as feedback to identify meaningful regions, the system receives accurate feedback about its performance on semantically relevant content, preventing perverse incentives during training.
3Productivity
If temporal consistency is evaluated without ground truth consistency filtering, then the evaluation process is faster and simpler, but it includes pixels that cannot provide meaningful evaluation information
Solution Approach 1:
The patent performs preliminary action by first identifying semantically meaningful regions through ground truth consistency checking before performing the temporal consistency evaluation. This preliminary segmentation ensures that subsequent evaluation operations only process pixels that can provide meaningful information, optimizing both accuracy and computational efficiency.
Data Source
AI summary
A computer-implemented method for evaluating a machine learning system for semantic segmentation of video data. The method includes: video frames, segmentation frames for the video frames, and at least one target segmentation frame are provided for a video frame; a relative movement between a camera used to record the video data and the scene shown in the video frames is ascertained; an expected segmentation frame is ascertained from at least one segmentation frame using the ascertained relative movement; a ground truth consistency is ascertained that indicates the extent to which the actual segmentation frame, and/or the expected segmentation frame, is consistent with a predetermined target segmentation frame for the video frame; a temporal consistency is ascertained that indicates the extent to which pixels or other parts of the actual segmentation frame are consistent with corresponding pixels or other parts of the expected segmentation frame, or the actual segmentation frame.