The goal of this project is to develop a machine-learning-enabled video coding strategy for object detection. Most existing video encoders minimizes distortion under a rate constraint. However, for surveillance video, it is desired for a video encoder to maximize detection probability under a rate constraint. To address this, we will design a new video coding strategy that maximizes object detection probability under a rate constraint. We will locate the information important to object detector, develop Rate-Detection-Optimized framework for mode selection, and design optimized bit rate allocation method.