Executing video analytics tasks using a large camera network is a challenging problem in the field of video processing. Video compression is a necessary step to reduce video data size before transmission. However, the performance of video analytics tasks generally degrade as video quality drops. This paper considers how to find the optimal point between video compression and performance for the video analytics task of activity recognition. We propose a system that predicts the success or failure of a video analytics task under different compression parameters without executing the task. The system is designed to automatically select the best compression rate for each video to maintain an acceptable detection accuracy. Our experiments indicate that such a system has the potential to improve overall performance across a variety of different activity sets selected from the UCF101 dataset [1].