Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification
It addresses vehicle recognition for applications like surveillance or traffic analysis, but is incremental as it surveys and compares existing methods.
This paper tackles vehicle attribute recognition by appearance, surveying algorithms for classifying vehicle type, make, and model, and compares classification and metric learning approaches in a simulated real-world scenario.
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.