Streamlining Action Recognition in Autonomous Shared Vehicles with an Audiovisual Cascade Strategy

With the advent of self-driving cars, and big companies such as Waymo or Bosch pushing forward into fully driverless transportation services, the in-vehicle behaviour of passengers must be monitored to ensure safety and comfort. The use of audio-visual information is attractive by its spatio-temporal richness as well as non-invasive nature, but faces tile likely constraints posed by available hardware and energy consumption. Hence new strategies are required to improve the usage of these scarce resources. We propose the processing of audio and visual data in a cascade pipeline for in-vehicle action recognition. The data is processed by modality-specific sub-modules. with subsequent ones being used when a confident classification is not reached. Experiments show an interesting accuracy-acceleration trade-off when compared with a parallel pipeline with late fusion, presenting potential for industrial applications on embedded devices.