Trajectory-Based Video Streaming Suppression for Security Cameras
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Solution Overview
Problem
Security camera systems often waste battery life and resources by continuously streaming video data due to motion detection from individuals and pets within monitored areas, as they cannot differentiate between interesting and uninteresting movements.
Innovation Solution
A central processing device learns and stores trajectories of individuals and pets, sending suppression signals to camera devices to halt video streaming when motion corresponds to common, uninteresting patterns, thereby reducing power consumption and extending battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion detection is used to initiate video streaming, then security monitoring capability is improved, but battery life deteriorates due to continuous streaming
Solution Approach 1:
The system performs preliminary analysis of motion patterns by tracking trajectories of detected objects over time. It learns and stores common movement patterns (e.g., family members walking through the house) before actual video streaming is needed. When motion is detected, the system checks against stored trajectory patterns first, and only streams video when the pattern doesn't match known benign movements, thus avoiding unnecessary streaming while maintaining security monitoring.
2Measurement precision
If video streaming is continuously performed upon motion detection, then detection accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of performing full video streaming for every motion detection event, the system applies partial action by first performing lightweight trajectory analysis and pattern matching. It only activates full video streaming when necessary (when motion patterns don't match stored trajectories), performing excessive analysis (trajectory tracking) only when needed to avoid the more energy-intensive video streaming operation.
3Loss of information
If all motion events are monitored and streamed, then information completeness is improved, but data transmission volume increases
Solution Approach 1:
The system extracts and separates meaningful motion events from routine movements by implementing trajectory-based filtering. It extracts only those motion events that represent novel or potentially significant activities (those not matching stored trajectories) for video streaming, while filtering out routine movements. This extraction process maintains information completeness for important events while eliminating unnecessary data transmission for routine activities.
Data Source
AI summary
Techniques are generally described for suppressing video streaming based on trajectory information. First video data captured at a first time may be received from a first camera device. A determination may be made that the first video data includes image data representing a previously-identified human. A determination may be made that first trajectory data associates movement of the previously-identified human with the first camera device at the first time. A signal may be sent to a second camera device. The signal may be effective to suppress streaming of video captured by the second camera device during a second time following the first time.