Energy-Aware Operation of Black Box Surveillance Cameras under Event Uncertainty and Memory Constraint

In this paper, we propose an event-driven black box surveillance camera which reduces energy consumption by waking up the system only when an event is detected and dynamically adjusting the video encoding and the resultant image distortion according to the criticality of captured frames called significance level. To achieve this goal, we find an encoding bitrate minimizing the energy consumption of the camera while satisfying the limited memory space constraint and distortion requirement at each significance level by judiciously allocating bit-rate to each significance level. To do that, we considered the trade-off relations between the total energy consumption vs. encoding bit-rate according to the significance level. For further energy savings, we also proposed a low complexity solution which adjusts the energy-minimal encoding bit-rate based on the dynamically changing event behavior, i.e., timing and duration of events. Experimental results show that the proposed method yields up to 67.49% (49.19% on average) energy savings compared to the conventional bitrate allocation methods.