Proposals on weighing passengers and their carry-on luggage prior to flights are gaining traction in the airline industry for fuel efficiency purposes. Adoption of such proposals are difficult in practice, however, as requiring passengers to step on weighing scales would incur significant overhead heavily affecting already busy airports. To solve this problem, we propose CamWeight, a novel vision-based weight inference system that takes video feed of off-the-shelf elastic mat (e.g., yoga mat) placed on the floor as the passenger walks over it while pulling his/her wheeled carry-on luggage. CamWeight makes use of inherent properties including amplitude and recovery time of strain, or mat deformation caused by footsteps and luggage wheels. Due to the inherent design of CamWeight, it incurs no additional time for weighing, while being cost effective. We present a preliminary proof-of-concept evaluation by varying weights in a luggage from 2.5 kg to 20 kg to achieve prediction mean absolute error of approximately 2 kg.