  Utilizing a single camera for measuring object distances is a cost-effective
alternative to stereo-vision and LiDAR. Although monocular distance estimation
has been explored in the literature, most existing techniques rely on object
class knowledge to achieve high performance. Without this contextual data,
monocular distance estimation becomes more challenging, lacking reference
points and object-specific cues. However, these cues can be misleading for
objects with wide-range variation or adversarial situations, which is a
challenging aspect of object-agnostic distance estimation. In this paper, we
propose DMODE, a class-agnostic method for monocular distance estimation that
does not require object class knowledge. DMODE estimates an object's distance
by fusing its fluctuation in size over time with the camera's motion, making it
adaptable to various object detectors and unknown objects, thus addressing
these challenges. We evaluate our model on the KITTI MOTS dataset using
ground-truth bounding box annotations and outputs from TrackRCNN and EagerMOT.
The object's location is determined using the change in bounding box sizes and
camera position without measuring the object's detection source or class
attributes. Our approach demonstrates superior performance in multi-class
object distance detection scenarios compared to conventional methods.
