A device to trigger a vehicular action based on monitoring one or more occupants of a vehicle, the device comprising one or more processors configured to: identify a plurality of features in a plurality of subsets of image data detailing the one or more occupants, the image data being video frames obtained from a video taken of the one or more occupants of the vehicle and the plurality of subsets being based on the video frames, wherein the one or more processors are configured to identify the plurality of features by providing inputs, based on the plurality of subsets of image data, to a two-dimensional convolutional neural network, the two-dimensional convolutional neural network including a fully connected layer; track changes over time of the plurality of features over the plurality of subsets of image data by providing the plurality of features in the plurality of subsets of image data as inputs to a spatio-temporal model, the spatio-temporal model comprising a plurality of convolutional kernels to track changes in the values corresponding to a respective feature across the series of segments, wherein the two-dimensional convolutional neural network provides an output comprising the identified plurality of features in each of the plurality of subsets of image data, wherein the output comprises a plurality of segments, wherein each of the plurality of segments corresponds to a respective subset of the plurality of subsets of image data, and wherein each segment comprises a plurality of values, wherein each value of the plurality of values corresponds to a respective feature of the plurality of features, wherein the value for each respective feature ranges from a minimum to a maximum, wherein the minimum indicates that the respective feature is non-existent in the segment and an increase in the value towards the maximum indicates that the respective feature is more prevalent in the segment; determine a state, from a plurality of states, of the one or more occupants based on the tracked changes; and trigger the vehicular action based on the determined state. identify a plurality of features in a plurality of subsets of image data detailing the one or more occupants, the image data being video frames obtained from a video taken of the one or more occupants of the vehicle and the plurality of subsets being based on the video frames, wherein the one or more processors are configured to identify the plurality of features by providing inputs, based on the plurality of subsets of image data, to a two-dimensional convolutional neural network, the two-dimensional convolutional neural network including a fully connected layer; track changes over time of the plurality of features over the plurality of subsets of image data by providing the plurality of features in the plurality of subsets of image data as inputs to a spatio-temporal model, the spatio-temporal model comprising a plurality of convolutional kernels to track changes in the values corresponding to a respective feature across the series of segments, wherein the two-dimensional convolutional neural network provides an output comprising the identified plurality of features in each of the plurality of subsets of image data, wherein the output comprises a plurality of segments, wherein each of the plurality of segments corresponds to a respective subset of the plurality of subsets of image data, and wherein each segment comprises a plurality of values, wherein each value of the plurality of values corresponds to a respective feature of the plurality of features, wherein the value for each respective feature ranges from a minimum to a maximum, wherein the minimum indicates that the respective feature is non-existent in the segment and an increase in the value towards the maximum indicates that the respective feature is more prevalent in the segment; determine a state, from a plurality of states, of the one or more occupants based on the tracked changes; and trigger the vehicular action based on the determined state.