In this work, we examine the resiliency of a state-of-the-art end-to-end video summarization (VS) application that serves as a representative emerging workload in the domain of real time edge computing. The VS application constitutes key video and image analytic elements that are processed by embedded systems aboard unmanned aerial vehicles (UAVs). Real-time performance and energy constraints motivate the consideration of approximations to the VS algorithm. However, mission-critical UAV applications also demand stringent levels of resilience to soft errors that are exacerbated with higher altitude. In this work, we study the effects of three different types of software approximations on the application level resiliency (to soft errors) of the VS algorithm. We show that our approximations yield significant energy savings (up to 68%), with commensurate improvement in performance, without a degradation in the application resilience. Further, by proposing a novel quality metric (appropriate for the UAV vision analytics domain) for the summarized video output, we show that even though the rate of Silent Data Corruptions (SDCs) increases slightly (<2%), the impact of these SDCs on output quality is limited. Thus, we conclude that software approximation can be utilized to achieve significant gains in performance and energy without affecting application resiliency.