Shadow has very specific properties that makes it very clear way of making it distinguishable from the regular object. A lot of work in the area of background subtraction and surveillance has been using this to eliminate the shadows or to avoid them being mistaken as the actual object (or human).
As observed by Daniel Grest
To distinguish the shadows from the person in the segmented image we
assume the following properties:
- a shadow pixel is darker than the corresponding pixel in the background image,
- the texture of the shadow is correlated with the corresponding texture of the background image.
This paper models in detail the property of color correlation and develops the Nromalized Cross-Corelation matrix method to identify the shadow pixels.
See 1 for more details.
Julio Cezar uses and extends the performance by localization. It also uses it in monochromatic conditions which would be harder than color. See 2 for details.
Horprasert characterizes shadow as
Shaded background or shadow (S) if it has similar chromaticity but
lower brightness than those of the same pixel in the background image.
This is based on the notion of the shadow as a semi-transparent region
in the image, which retains a representation of the underlying surface
pattern, texture or color value
This technique is used to classify scene in background, shadow and a moving object. See 3 for more details.
In a sense, these papers are indicating the properties you mentioned - but they show the mathematical model for the same which allows computationally identifying the shadows.
Daniel Grest et. al. A Color Similarity Measure for Robust Shadow Removal in Real-Time
Julio Cezar et. al. Background Subtraction and Shadow Detection in Grayscale Video Sequences
Thanarat Horprasert, David Harwood A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection