Data is the unreasonably effective force behind the current deep learning breakthroughs. Without a sufficient amount of data, even the most intricate neural network powered by the best hardware would fall short of human-level performance. As video data is becoming ubiquitous, we will rely on machines to reason and extract information from numerous videos made available by social media and visual-enabled devices. Supervised learning will drive the most commercial successes in deep learning but its data collection process is flawed. Finding no suitable video dataset for teaching machines to understand the world, we developed crowd-acting, an industrial data collection approach inspired by previous contributions, particularly Hollywood in Homes and its dataset Charades (Sigurdsson et al.).