Vehicle classification has evolved into a significant subject of study due to its importance in autonomous navigation, traffic analysis, surveillance and security systems, and transportation management. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear-side view where the camera's field of view is directly behind the vehicle.
Prof. Bhanu and his colleagues from the University of California, Riverside have developed a method for recognizing the logo of a vehicle from a low-resolution video feed in real-time. This method works by using a software system for super-resolving the vehicle maker's logos, which facilitates recognition of a vehicle make more reliably than low-resolution vehicle logos. A super-resolution algorithm produces a high-resolution image from low-resolution video input.
Sample super-resolution results comparison - from left to right (a) Low-resolution images (enlarged by pixel replication), (b) Bicubic interpolation (c) Kernel Regression, (d) Iterative Curve Based Interpolation, (e) Adaptive Sparse Domain Selection, and (f) Current invention. For display purpose all the images are normalized to the same size.
To identify the logo of a vehicle from a low-resolution video feed in real-time in applications such as:
Country | Type | Number | Dated | Case |
United States Of America | Issued Patent | 10,127,437 | 11/13/2018 | 2012-885 |
United States Of America | Issued Patent | 9,928,406 | 03/27/2018 | 2012-885 |
vehicle logo identification, super-resolution image, real-time, low-resolution