  As a result of significant advances in deep learning, computer vision
technology has been widely adopted in the field of traffic surveillance.
Nonetheless, it is difficult to find a universal model that can measure traffic
parameters irrespective of ambient conditions such as times of the day,
weather, or shadows. These conditions vary recurrently, but the exact points of
change are inconsistent and unpredictable. Thus, the application of a
multi-regime method would be problematic, even when separate sets of model
parameters are prepared in advance. In the present study we devised a robust
approach that facilitates multi-regime analysis. This approach employs an
online parametric algorithm to determine the change-points for ambient
conditions. An autoencoder was used to reduce the dimensions of input images,
and reduced feature vectors were used to implement the online change-point
algorithm. Seven separate periods were tagged with typical times in a given
day. Multi-regime analysis was then performed so that the traffic density could
be separately measured for each period. To train and test models for vehicle
counting, 1,100 video images were randomly chosen for each period and labeled
with traffic counts. The measurement accuracy of multi-regime analysis was much
higher than that of an integrated model trained on all data.
