Video Surveillance Object Detection Training With Same-View 3D Simulation
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Solution Overview
Problem
Existing machine learning algorithms for video surveillance face challenges in accurately identifying objects and activities due to differences in imaging conditions and scene content between training data and real-world deployment, which can lead to concept drift and insufficient training periods.
Innovation Solution
A 3D simulation of the real environment is generated from video surveillance data, synthesizing objects and activities to create training data viewed from the camera's intended viewpoint, allowing for tailored and diverse training without human supervision, addressing privacy concerns and enabling simulation of various scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard training data from diverse sources is used, then the algorithm can be trained on various conditions, but the training data does not accurately reflect specific deployment scene conditions leading to concept drift
Solution Approach 1:
The patent changes the parameters of the training data by generating synthetic data with controllable scene parameters (lighting conditions, weather, time of day, seasonal variations) that match the specific deployment environment. This allows the algorithm to be trained on data that accurately reflects the target scene conditions, resolving the mismatch between standard training data and specific deployment scenarios.
Solution Approach 2:
The patent creates a virtual copy of the real deployment environment through 3D simulation. The synthetic training data replicates the specific scene conditions (imaging conditions, viewpoint, objects present) of the actual deployment location, providing a faithful copy that enables accurate object detection without requiring physical training data collection from the real environment.
2Loss of time
If a short training period (2 weeks) is used, then the system can be deployed quickly, but concept drift occurs due to longer term changes in imaging conditions and object types
Solution Approach 1:
The patent performs preliminary action by pre-generating comprehensive synthetic training data that anticipates future concept drift. The 3D simulation environment can model long-term changes in imaging conditions, seasonal variations, and emerging object types before they occur in the real deployment, allowing the algorithm to be pre-trained on these future scenarios.
Solution Approach 2:
The patent introduces dynamics by making the training data generation process adaptive to changing conditions. The 3D simulation environment can dynamically adjust scene parameters to reflect evolving imaging conditions, weather patterns, and object types over time, enabling continuous adaptation without requiring extended real-world training periods.
3Measurement precision
If real-world training data is collected, then the algorithm learns from actual conditions, but privacy concerns arise and data collection is time-consuming
Solution Approach 1:
The patent creates a virtual copy of real-world scenes through 3D simulation, generating synthetic training data that replicates real conditions without capturing actual people, places, or events. This copying approach preserves the essential visual characteristics needed for accurate detection while eliminating privacy concerns and the time required to collect real-world data.
Solution Approach 2:
The patent introduces an intermediary layer (3D simulation environment) between the real world and the training process. This intermediary generates synthetic data that mediates between the need for real-condition training data and the constraints of privacy and time, producing training data that is both realistic and safe to use.
4Adaptability or versatility
If rare events and uncommon objects are included in training data, then the algorithm becomes more comprehensive, but the training data becomes more difficult to collect and process
Solution Approach 1:
The patent uses 3D simulation to create virtual copies of rare events and uncommon objects that would be difficult to capture in real-world data. The simulation environment can generate diverse scenarios (traffic accidents, unusual vehicle models, rare activities) with complete ground truth labels, simplifying the data collection and processing complexity while expanding algorithm coverage.
Solution Approach 2:
The patent performs preliminary generation of training data for rare events and uncommon objects through 3D simulation before they occur in the real world. This allows comprehensive training data to be prepared in advance for edge cases and rare scenarios, avoiding the complexity of collecting and verifying such data from actual deployments.
Data Source
AI summary
A method of training a machine learning algorithm to identify objects or activities in video surveillance data comprises generating a 3D simulation of a real environment from video surveillance data captured by at least one video surveillance camera installed in the real environment. Objects or activities are synthesized within the simulated 3D environment and the synthesized objects or activities within the simulated 3D environment are used as training data to train the machine learning algorithm to identify objects or activities, wherein the synthesized objects or activities within the simulated 3D environment used as training data are all viewed from the same viewpoint in the simulated 3D environment.