In this paper, we study the problem of recognizing car brands in surveillance videos, cast it as an image classification problem, and propose a novel multiple instance learning method, named Spatially Coherent Discriminative Pattern Learning, to discover the most discriminative patterns in car images. The learned discriminative patterns can effectively distinguish cars of different brands with high accuracy and efficiency. The experimental results demonstrate that our method is significantly superior to recent image classification methods on this problem. The proposed method is able to deliver an end-to-end real-time car recognition system for video surveillance. Moreover, we construct a large and challenging car image data set, consisting of 37 195 real-world car images from 30 brands, which could serve as a standard benchmark in this field and be used in various related research communities.
Learning Discriminative Pattern for Real-Time Car Brand Recognition
IEEE Transactions on Intelligent Transportation Systems ; 16 , 6 ; 3170-3181
2015-12-01
2290804 byte
Article (Journal)
Electronic Resource
English
Learning Discriminative Pattern for Real-Time Car Brand Recognition
Online Contents | 2015
|Graph-Based Discriminative Learning for Location Recognition
British Library Online Contents | 2015
|Discriminative local binary pattern
British Library Online Contents | 2016
|A Hybrid Generative-Discriminative Learning Algorithm for Image Recognition
Springer Verlag | 2017
|Object recognition using discriminative parts
British Library Online Contents | 2012
|