Microfluidics technology targets droplet manipulation by confining fluids to manufacture materials for many industrial applications and life sciences. Data analysis obtained by microfluidic experiments remains a bottleneck to this day due to the lack of a reliable interface converting raw observations to informative data fast enough and at low operating costs. Many computer vision tools exist, which permit to infer useful physical information from video data but they typically target specific physical systems and lack broad applicability. Moreover, the existing tools need intensive development and fine-tuning before they can be useful in a specific scenario. Thus, such a lack of computer vision tools with broad applicability hinders the penetration of this technology for extensive microfluidics applications. This PoC aims to develop a stand-alone, easy-to-use software (DropTrack) that can detect and track individual droplets from experimental videos. Two cutting-edge deep-learning algorithms, YOLO for droplet detection and DeepSORT for droplet tracking, will be at the software's core. DropTrack will provide trajectories of the individual droplets and measure physical quantities of interest, such as the droplet numbers, flow rates, packing fraction, etc., by analyzing videos from experiments. Our preliminary work indicates that DropTrack is capable of analyzing images much faster than the image capture rate of a typical digital camera, thereby opening entirely new prospects for real-time feedback control in experiments. The realization of DropTrack will lead to dramatic time and resources savings in video data analysis of deformable moving objects in microfluidics. Beyond this PoC, the successful demonstration of this technology in microfluidics is expected to attract significant attention as a handy data analysis tool in other relevant fields such as multi-organism biological systems from cell aggregates to animal congregation.