Video Analytics Pipeline Framework for Custom Model Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video analytics systems in the surveillance industry face challenges due to data and scene variability, limited model applicability, and a disconnect between researchers and end-users, leading to high false alarms and limited performance in real-world scenarios.
Innovation Solution
A pipeline framework that allows users to annotate datasets, train custom computer vision algorithms, and perform analytics through various modules for preprocessing, pattern recognition, and statistical analysis, enabling scalable and flexible video analysis on multiple streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vision algorithms are designed and optimized on datasets to encapsulate real world scenarios, then the algorithms can achieve good performance on training data, but the performance is unknown in new scenarios leading to higher false alarms
Solution Approach 1:
The system dynamically adapts vision algorithms by allowing users to adjust parameters and retrain models based on specific scene conditions. The framework enables dynamic modification of algorithm behavior to match varying real-world scenarios, transitioning from static pre-trained models to adaptive systems that can handle data and scene variability.
Solution Approach 2:
The framework allows users to change algorithm parameters and retrain models with custom datasets specific to their scenarios. By modifying training data and algorithm parameters, the system optimizes performance for specific applications while reducing false alarms in new scenarios.
2Ease of manufacture
If a black boxed analytic based on one method is used, then the system is simple to deploy, but the applicability is limited in other scenarios
Solution Approach 1:
The framework provides a universal platform that can accommodate multiple vision algorithms and methods within a single system. Users can select and switch between different algorithms (e.g., density-based crowd counting, detection-based crowd counting) based on scenario requirements, making the system versatile across various applications while maintaining ease of deployment through a unified interface.
Solution Approach 2:
The system transitions from a static black-box approach to a dynamic configurable framework where users can adjust algorithm selection, parameter settings, and model configurations based on specific scenario needs, enabling the same system to adapt to diverse applications.
3Adaptability or versatility
If users are given the power to build, customize and perform analytics, then the system becomes more adaptable to specific tasks, but the device complexity increases
Solution Approach 1:
The framework enables users to independently annotate datasets, train custom models, and configure analytics pipelines without requiring expert knowledge from researchers or software developers. The system provides self-service capabilities through intuitive interfaces that guide users through the customization process, reducing the complexity burden despite increased adaptability.
Solution Approach 2:
The framework acts as an intermediary layer between raw vision algorithms and end-users, providing abstraction through modular pipelines and configuration interfaces. This mediator simplifies the interaction complexity while preserving full customization capability, allowing users to leverage powerful algorithms without directly managing their complexity.
4Measurement precision
If data driven algorithms are trained on annotated datasets to accomplish specific tasks, then the algorithms achieve task-specific performance, but retraining requires specific data that may not be available to users
Solution Approach 1:
The framework empowers users to independently annotate their own datasets and retrain models for their specific tasks without relying on pre-packaged datasets created by researchers. Users can collect, annotate, and use their own data to train algorithms, making the retraining process accessible and eliminating dependency on external data availability.
Data Source
AI summary
Preferred embodiments described herein relate to a pipeline framework that allows for customized analytic processes to be performed on multiple streams of videos. An analytic takes data as input and performs a set of operations and transforms it into information. The methods and systems disclosed herein include a framework (1) that allows users to annotate and create variable datasets, (2) to train computer vision algorithms to create custom models to accomplish specific tasks, (3) to pipeline video data through various computer vision modules for preprocessing, pattern recognition, and statistical analytics to create custom analytics, and (4) to perform analysis using a scalable architecture that allows for running analytic pipelines on multiple streams of videos.