Type-Specific Video Object Tracking With Machine Learning
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Solution Overview
Problem
Law enforcement agencies face challenges in efficiently identifying and managing large volumes of video data captured by body-worn devices due to the tedious and time-consuming process of sifting through irrelevant content, necessitating privacy and legal considerations.
Innovation Solution
A system utilizing multiple machine-learning models trained for specific object types, allowing users to selectively apply these models for object detection, tracking, and redaction within video frames, with user interface controls for correction and training of models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of video content is performed to identify relevant data, then accuracy in identifying specific video content can be maintained, but time consumption and labor resources increase significantly
Solution Approach 1:
The system segments the video review task by dividing it into multiple stages: automated preprocessing using machine learning models to identify and tag relevant segments, followed by selective manual review only of those identified segments. This segmentation allows the system to maintain accuracy while reducing the overall time and resources required, as manual reviewers only need to verify AI-identified content rather than review entire video files.
Solution Approach 2:
The patent introduces an intermediary system comprising machine learning models and automated video analysis tools that act as a bridge between raw video data and manual review processes. This intermediary layer pre-processes video content, identifies relevant segments, and prioritizes them for manual review, thereby maintaining accuracy while significantly reducing the time and effort required for complete manual review.
2Measurement precision
If multiple machine-learning models are used for object detection, then object recognition accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system dynamically adjusts the number and type of machine learning models activated based on real-time conditions such as video content characteristics, user preferences, and computational resource availability. This dynamic approach allows the system to maintain high object recognition accuracy when needed while reducing computational complexity during routine processing or when resources are constrained.
Solution Approach 2:
Different machine learning models are selectively applied to different regions or types of video content based on their specific characteristics. For example, more computationally intensive models are applied only to segments where high precision is critical or where simpler models have failed, rather than applying all models uniformly to entire video files. This local quality approach maintains accuracy where needed while reducing overall computational complexity.
Data Source
AI summary
A video file may be presented via a user application that displays one or more video frames of the video file. A user request to perform an object detection for objects of a specific object type in a video frame of the video file may be received from the user application. A machine-learning model of a plurality of machine-learning models that is configured to detect objects of the specific object type may be applied to the video frame to detect an object of the specific object type in the video frame. Each of the plurality of machine-learning models may be trained to detect objects of a corresponding object type. Subsequently, an object tracking algorithm may be applied to one or more additional video frames of the video file to track the object of the specific object type across the one or more additional video frames.