Pose-Based Live Video Rate Control for Reliable Low-Power Monitoring

The system dynamically adjusts the data rate of the video stream based on detected pose categories. When a living being is detected in a pose category of interest, the system increases the data rate to high quality for reliable analysis. When no relevant poses are detected, the system reduces the data rate to save power and bandwidth. This dynamic adaptation resolves the contradiction by making power consumption variable rather than constant.

The system changes the data rate parameter of the video stream based on the detected pose category. By monitoring pose categories and adjusting the data rate parameter accordingly, the system maintains high event detection reliability when needed while reducing power consumption during normal conditions. This parameter change approach allows the system to operate efficiently across different operational states.

The system dynamically adjusts the data rate based on detected pose categories. High data rates are applied only when living beings are detected in poses of interest, ensuring reliable event detection. During periods without relevant activity, the system reduces data rates to minimize bandwidth consumption. This dynamic approach resolves the bandwidth contradiction by making data transmission variable rather than continuous at full capacity.

The system changes the data rate parameter in response to detected pose categories. By monitoring the pose category parameter and adjusting the data rate accordingly, the system maintains high bandwidth utilization only when necessary for reliable event detection, while reducing bandwidth requirements during normal operating conditions.

The system employs periodic action by alternating between low-power monitoring mode and high-power analysis mode. The system continuously monitors pose categories at low power consumption, then activates full AI analysis capability only when a pose category of interest is detected. This periodic activation pattern maintains event detection capability while preventing continuous overheating, as the high-power operations are intermittent rather than continuous.

The system dynamically adjusts its operational power state based on detected pose categories. When no relevant poses are detected, the system operates in a low-power state that prevents overheating. When poses of interest are detected, the system transitions to a high-power state to maintain event detection capability. This dynamic power management resolves the temperature contradiction by making power consumption adaptive rather than constant.

The system changes the data rate parameter based on detected pose categories to achieve cost-effectiveness without sacrificing reliability. When living beings are detected in poses of interest, the system increases the data rate to ensure reliable event detection. When no relevant poses are detected, the system reduces the data rate to save power and bandwidth, improving cost-effectiveness. This conditional parameter change resolves the contradiction by making reliability dependent on actual event presence rather than constant high data rates.

The system applies different data rates to different temporal periods based on local conditions (detected pose categories). During periods with relevant activity, high data rates are applied locally to ensure detection reliability. During periods without relevant activity, low data rates are applied locally to improve cost-effectiveness. This local quality approach allows the system to optimize both reliability and cost-effectiveness in different operational contexts rather than using a uniform data rate.