Neural Network Training With Sparse 3D Cuboid Video Labels
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Solution Overview
Problem
Existing machine learning models for object detection in vehicle environments require large amounts of densely labeled video sequences, which are inefficient to process due to excessive object labels, leading to high computational resource usage.
Innovation Solution
Convert densely labeled video sequences to sparsely labeled sequences by using the Douglas-Peucker algorithm to remove labeled cuboids within a user-defined distance from a line connecting keyframes, maintaining linear object positions and orientations, thereby reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If densely labeled video sequences are used for training machine learning models, then object detection accuracy is improved, but computational resource usage increases significantly
Solution Approach 1:
The patent extracts and removes unnecessary object labels from video sequences using the Douglas-Peucker algorithm. By identifying and eliminating redundant labeled cuboids that do not contribute significantly to object detection accuracy, the system reduces computational resource usage while maintaining detection performance. This selective removal of excess data directly addresses the contradiction between using dense labels for accuracy and the high computational cost they incur.
2Quantity of substance
If densely labeled video sequences are used for training, then training data completeness is improved, but processing efficiency deteriorates
Solution Approach 1:
The system extracts essential object trajectory information while removing redundant labeled cuboids from densely labeled video sequences. By applying the Douglas-Peucker algorithm to identify and eliminate unnecessary labels that do not contribute to object detection accuracy, the system maintains training data completeness for critical objects while significantly improving processing efficiency through reduced data volume.
Solution Approach 2:
The patent transforms densely labeled video sequences into sparsely labeled sequences by changing the density parameter of object labels. This parameter transformation reduces the quantity of training data from dense to sparse representation, thereby improving processing efficiency while preserving the essential object detection information needed for complete training.
3Measurement precision
If all labeled cuboids are retained in video sequences, then object tracking accuracy is improved, but data redundancy increases
Solution Approach 1:
The patent applies the Douglas-Peucker algorithm to extract and remove redundant labeled cuboids from video sequences. By identifying labels that deviate minimally from the object's trajectory line and removing them, the system eliminates data redundancy while preserving the essential information needed for accurate object tracking. This selective extraction maintains tracking precision by keeping only the necessary labels.
4Adaptability or versatility
If densely labeled video sequences are processed, then training model comprehensiveness is improved, but computational time increases
Solution Approach 1:
The system extracts and removes unnecessary labeled cuboids from densely labeled video sequences using the Douglas-Peucker algorithm. By eliminating redundant labels that do not contribute to model comprehensiveness, the system reduces computational time required for processing training data while maintaining the comprehensiveness of the trained model through preservation of essential object detection information.
Solution Approach 2:
The patent changes the label density parameter from dense to sparse representation, transforming the training data structure. This parameter change reduces the computational burden and processing time while maintaining the comprehensiveness of the training model by preserving the essential object trajectory and detection information needed for effective machine learning training.
Data Source
AI summary
A computer that includes a processor and a memory, the memory including instructions executable by the processor to plot three-dimensional positions of labeled cuboids from successive frames between a first keyframe and a second keyframe of a densely labeled video sequence. A sparsely labeled video sequence can be generated by removing labeled cuboids from the densely labeled video sequence with positions that are within a user-selected distance from a line extending from a position of the labeled cuboid in the first keyframe to a position of the labeled cuboid in the second keyframe. The sparsely labeled video sequence can be output for training a machine learning system.