Semantic Reasoning Engine for Object Interaction Detection
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Solution Overview
Problem
Current object detection systems in complex environments, such as crowded areas, struggle to accurately identify potentially dangerous interactions between objects due to limitations in training data and lack of explainability, especially when dealing with patterns outside their training datasets.
Innovation Solution
A deep fusion reasoning engine (DFRE) converts video data into tracklets, identifies objects as attractors or repulsors, and uses semantic reasoning to make inferences about interactions, providing data for display, leveraging both sub-symbolic and symbolic learning to analyze and reason about object interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning systems are used to detect conditions in surveillance footage, then the system can identify known patterns from training data, but it fails to identify conditions outside of its training datasets and lacks explainability
Solution Approach 1:
The system segments the detection process into multiple specialized modules: a deep learning module for pattern recognition, a semantic analysis module for contextual understanding, and a reasoning module for explainable inference. Each module handles specific aspects of the detection task, allowing the system to process both known and unknown patterns while maintaining explainability through the modular architecture.
Solution Approach 2:
The system combines multiple AI approaches into a composite detection framework that integrates deep learning's pattern recognition capabilities with semantic reasoning's explainability strengths. This composite architecture allows the system to leverage the advantages of different methodologies while mitigating their individual limitations, achieving both adaptability to novel conditions and transparent decision-making.
2Productivity
If complex systems like crowds and traffic are modeled using traditional methods, then the system can process the data, but it is notoriously hard to model and limited by training data capabilities
Solution Approach 1:
The system introduces semantic concepts and ontologies as intermediary layers between raw surveillance data and detection conclusions. These intermediaries provide a structured framework for representing complex interactions in crowds and traffic, making the modeling process more manageable and less dependent on exhaustive training data while maintaining high processing capability.
3Measurement precision
If systems simply match known patterns from training data, then the system can provide consistent results for trained conditions, but it is ineffectual at identifying conditions outside of training datasets
Solution Approach 1:
The system employs dynamic detection mechanisms that adapt to both known and unknown conditions. The deep learning components provide precise pattern matching for trained conditions, while the semantic reasoning components dynamically adjust to interpret novel situations by analyzing contextual relationships and physical interactions, maintaining precision while enhancing adaptability.
Data Source
AI summary
In one embodiment, a device converts video data into a set of tracklets, each tracklet representing a different object depicted in the video data. The device identifies a particular object depicted in the video data as being an attractor or repulsor with respect to one or more other objects depicted in the video data, based on an analysis of their respective tracklets. The device makes, using a semantic reasoning engine, an inference about the video data, based in part on the particular object being identified as an attractor or repulsor. The device provides data based on the inference for display.