AI Video Annotation System Penal Code Prediction
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Solution Overview
Problem
Law enforcement officers face challenges in determining the specific penal code violation from video evidence, as existing methods lack accuracy and efficiency in analyzing and annotating criminal acts captured in surveillance footage.
Innovation Solution
An automated video annotation system utilizing AI to summarize video content, identify the most likely penal code violation, and modify annotations based on a lexicon associated with the identified code, with reinforcement learning for improved accuracy through feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If law enforcement officers manually analyze video evidence to determine penal code violations, then they can review and interpret the content, but the process lacks efficiency and accuracy in identifying specific crimes
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated AI-based video annotation system. The system uses machine learning models to automatically generate text annotations from video content, identify penal code violations, and suggest charges, thereby substituting human manual review with automated computational analysis to improve both accuracy and efficiency
Solution Approach 2:
The system enables self-service by allowing the video annotation system to autonomously analyze footage, generate descriptions, identify potential penal code violations, and provide charge suggestions without requiring constant human intervention. The automated process serves itself by continuously processing video evidence and generating legal annotations independently
2Adaptability or versatility
If the penal code contains many different crimes with similar elements, then comprehensive coverage is achieved, but selecting the correct penal code becomes difficult
Solution Approach 1:
The system segments the complex penal code selection process into distinct analytical components: video content analysis, text annotation generation, keyword extraction, penal code matching, and charge suggestion. By breaking down the selection process into manageable segments, the system can handle comprehensive crime coverage while reducing the complexity of selecting the correct penal code
Solution Approach 2:
The patent introduces text annotations as an intermediary layer between video content and penal code selection. The system first generates detailed text descriptions of video events, then uses these annotations as intermediaries to match against penal code elements, thereby simplifying the complex mapping between diverse crimes and their corresponding legal provisions
3Reliability
If similar conduct can be charged under different penal codes with significantly different penalties, then legal precision is required, but determining the correct charge becomes challenging
Solution Approach 1:
The system applies local quality by analyzing specific elements of video content and matching them against corresponding elements in penal code definitions. Rather than treating all crimes uniformly, the system focuses on identifying and comparing specific local characteristics (such as type of contact, injury level, intent indicators) to distinguish between similar crimes like assault and battery with high reliability
Data Source
AI summary
Techniques for predicting a penal code and modifying an annotation based on the prediction are provided. A sentence describing a scene captured with a video capture device may be received form a video description system. The scene may depict a criminal act. An artificial intelligence bot may identify a penal code that has the highest probability of being associated with the criminal act described by the sentence. The sentence may be modified based on a lexicon associated with the identified penal code. Feedback indicating if the identification of the penal code was correct may be received. The feedback may be used to train the artificial intelligence bot.