Interframe Difference Image Augmentation for Motion Detection
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Solution Overview
Problem
Existing motion detection systems face challenges in creating comprehensive training sets for deep learning models, particularly in capturing diverse scenarios of object movement under varying conditions, leading to imbalanced datasets with insufficient samples for complex multi-object or inclement weather scenarios.
Innovation Solution
The system combines interframe difference images to generate new training samples that represent multiple objects or scenarios, using methods such as averaging, weighted averaging, and spatial transformations to create additional training data, thereby augmenting the training set with more diverse motion signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If interframe difference images are used as training samples for motion detection, then the training process becomes computationally simple and captures motion signatures effectively, but the training set becomes imbalanced with insufficient samples for complex multi-object or inclement weather scenarios
Solution Approach 1:
The patent combines multiple interframe difference images showing different motion scenarios (single object motion, multi-object motion, non-OI motion) through additive operations to generate composite training samples. This merging approach creates comprehensive training data that represents complex real-world scenarios while maintaining computational simplicity in the generation process.
Solution Approach 2:
The patent generates synthetic training samples by copying and combining existing interframe difference images through additive operations. Instead of requiring actual captured footage of every possible scenario, the system creates copies and combinations of available motion signatures to simulate complex scenarios like multi-object motion or motion under inclement weather conditions.
2Measurement precision
If the training set includes diverse scenarios such as multi-object motion and inclement weather conditions, then detection accuracy improves, but the complexity of creating and managing the training set increases significantly
Solution Approach 1:
The system performs self-service by automatically generating diverse training samples through additive operations on existing interframe difference images. The training set augmentation process is self-contained, requiring no external data collection or manual annotation for complex scenarios like multi-object motion or weather conditions, thereby reducing management complexity while improving detection accuracy.
Solution Approach 2:
The patent prepares comprehensive training samples in advance by combining various motion signatures into composite images before the actual detection task. This preliminary action creates a ready-to-use augmented training set that covers diverse scenarios, eliminating the need for complex real-time data management during deployment while ensuring high detection accuracy.
3Reliability
If more training samples are collected for rare scenarios like multi-object motion or single-object motion with non-OI motion, then the classification performance improves, but the time and resources required for data collection and annotation increase
Solution Approach 1:
The patent creates synthetic training samples for rare scenarios by copying and combining existing interframe difference images through additive operations. This approach generates unlimited variations of complex scenarios like multi-object motion without requiring actual field collection or manual annotation, dramatically reducing time investment while improving classification reliability for these rare but important cases.
Solution Approach 2:
The system varies parameters of existing training samples by combining them in different ways through additive operations. By changing the composition parameters (which images are combined, how they are weighted and aligned), the system generates diverse training variations that improve classification performance across different scenarios without requiring proportional increases in data collection time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an event detector. The methods, systems, and apparatus include actions of identifying a portion of a first interframe difference image that represents motion of an OI, determining that a second interframe difference image represents motion by a non-OI, combining the portion of the first interframe difference image and the second interframe difference image as a third interframe difference image labeled as motion of both an OI and a non-OI, and training an event detector with the third interframe difference image.