Video-Audio Activity Detection for Poor-Visibility Areas
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Solution Overview
Problem
Existing video surveillance systems struggle to identify activity in areas with poor visibility due to conditions like low lighting, obstructions, or adverse weather, making it difficult to visually detect human activity that may indicate potential problems.
Innovation Solution
A system utilizing a video camera and an audio sensor to capture and process video to identify visible events of interest, determine sound profiles, and compare them with audio streams to filter out irrelevant sounds, allowing for the detection of human activity through audio analysis even when video capture is impaired.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video surveillance is used to monitor activity in an area, then visual identification of events is improved, but under conditions of low lighting, heavy rain, or obstructions, the ability to identify activity deteriorates
Solution Approach 1:
The system merges video surveillance with audio surveillance by combining visual event detection with audio event detection and sound profile analysis. When video identification fails due to adverse conditions, the system switches to or supplements with audio-based activity identification, creating a multi-modal surveillance system that maintains reliability across varying environmental conditions.
Solution Approach 2:
The system introduces an intermediary decision-making layer that determines whether to rely on video or audio based on current conditions. The processor analyzes video legibility and selectively activates audio analysis when visual identification is compromised, acting as an intermediary that bridges the gap between visual and auditory detection modalities.
2Adaptability or versatility
If audio analysis is used to identify activity when video is unavailable, then activity detection capability is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing sound profiles for various events of interest and pre-determining the conditions under which audio analysis should be activated. During operation, when video legibility is compromised, the system can immediately switch to audio analysis using pre-configured sound profiles, avoiding the need for complex real-time decision-making and reducing operational complexity.
Solution Approach 2:
The system extracts and separates audio analysis as an independent module that can operate autonomously when video surveillance is ineffective. By extracting the audio processing chain (audio sensor, sound profile matching, event identification) as a distinct functional unit, the system manages complexity through modular design, allowing each modality to be developed, optimized, and maintained independently.
Data Source
AI summary
Identifying activity in an area even during periods of poor visibility using a video camera and an audio sensor are disclosed. The video camera is used to identify visible events of interest and the audio sensor is used to capture audio occurring temporally with the identified visible events of interest. A sound profile is determined for each of the identified visible events of interest based on sounds captured by the audio sensor during the corresponding identified visible event of interest. Then, during a time of poor visibility, a subsequent sound event is identified in a subsequent audio stream captured by the audio sensor. One or more sound characteristics of the subsequent sound event are compared with the sound profiles associated with each of the identified visible events of interest, and if there is a match, one or more matching sound profiles are filtered out from the subsequent audio stream.