  This paper studies vehicle attribute recognition by appearance. In the
literature, image-based target recognition has been extensively investigated in
many use cases, such as facial recognition, but less so in the field of vehicle
attribute recognition. We survey a number of algorithms that identify vehicle
properties ranging from coarse-grained level (vehicle type) to fine-grained
level (vehicle make and model). Moreover, we discuss two alternative approaches
for these tasks, including straightforward classification and a more flexible
metric learning method. Furthermore, we design a simulated real-world scenario
for vehicle attribute recognition and present an experimental comparison of the
two approaches.
