  Video processing solutions for motion analysis are key tasks in many computer
vision applications, ranging from human activity recognition to object
detection. In particular, speed estimation algorithms may be relevant in
contexts such as street monitoring and environment surveillance. In most
realistic scenarios, the projection of a framed object of interest onto the
image plane is likely to be affected by dynamic changes mainly related to
perspectival transformations or periodic behaviours. Therefore, advanced speed
estimation techniques need to rely on robust algorithms for object detection
that are able to deal with potential geometrical modifications. The proposed
method is composed of a sequence of pre-processing operations, that aim to
reduce or neglect perspetival effects affecting the objects of interest,
followed by the estimation phase based on the Maximum Likelihood (ML)
principle, where the speed of the foreground objects is estimated. The ML
estimation method represents, indeed, a consolidated statistical tool that may
be exploited to obtain reliable results. The performance of the proposed
algorithm is evaluated on a set of real video recordings and compared with a
block-matching motion estimation algorithm. The obtained results indicate that
the proposed method shows good and robust performance.
