Monitoring Image Sequence Sub-Sequence Determination
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Solution Overview
Problem
Current video-based vehicle interior monitoring systems face high computational and economic costs due to the need for extensive data analysis and storage of entire video sequences, which is inefficient and costly, especially when only a small portion of the data is relevant for identifying unusual occurrences.
Innovation Solution
A method that determines noteworthy sub-sequences of monitoring image sequences by correlating unusual audio and video signals, using neural networks to filter out non-relevant data and reduce the amount of data to be uploaded, focusing on interactions between passengers and drivers, thereby minimizing data transfer and storage costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire video sequences are stored and analyzed, then complete monitoring data is available, but data storage and transfer costs increase significantly
Solution Approach 1:
The video sequence is divided into individual frames, and only frames containing unusual movements are selected for further analysis. This segmentation allows the system to process only relevant portions of the data rather than the entire sequence, reducing storage and transfer costs while maintaining monitoring reliability.
Solution Approach 2:
The system extracts and isolates only the relevant information (frames with unusual movements) from the complete video sequence. By taking out only the necessary data for analysis, the system reduces the quantity of data to be stored and transferred while preserving the essential monitoring functionality.
2Measurement precision
If in-depth analysis methods are used to detect events, then event detection accuracy improves, but computational cost and hardware requirements increase
Solution Approach 1:
The system performs preliminary analysis by examining individual frames for unusual movements before conducting more complex temporal analysis. This preliminary action filters out irrelevant frames early, reducing the computational load required for subsequent event detection and classification.
Solution Approach 2:
Instead of applying complex analysis to the entire video sequence, the system applies analysis only to the partial set of frames that contain unusual movements. This partial action maintains detection accuracy for relevant events while significantly reducing overall computational requirements.
3Ease of operation
If traditional event classification methods are used, then event identification is possible, but the chicken-and-egg problem arises due to insufficient field data for defining events
Solution Approach 1:
The system automatically identifies and classifies events based on detected unusual movements without requiring extensive pre-defined event categories. The method serves itself by using the actual video data to inform event detection, reducing the dependency on pre-existing event definitions and allowing the system to adapt to real-world scenarios.
Data Source
AI summary
A method for determining a noteworthy sub-sequence of a monitoring image sequence of a monitoring area. The method includes: providing an audio signal from the monitoring area, at least partially including a time period of the monitoring image sequence; providing the monitoring image sequence of the environment to be monitored, which has been generated by an imaging system; determining at least one segment of the audio signal from the provided audio signal, which has unusual noises; determining at least one segment of the monitoring image sequence having unusual movements within the environment to be monitored; determining a correlation between the at least one segment of the audio signal having unusual noises and the at least one segment of the monitoring image sequence with unusual movements in order to determine a noteworthy sub-sequence of the monitoring image sequence.