PROJECT TITLE:

Learning Discriminative Pattern for Real-Time Car Brand Recognition

ABSTRACT:

In this paper, we have a tendency to study the problem of recognizing car brands in surveillance videos, forged it as an image classification downside, and propose a unique multiple instance learning method, named Spatially Coherent Discriminative Pattern Learning, to discover the foremost discriminative patterns in automobile images. The learned discriminative patterns can effectively distinguish cars of various brands with high accuracy and efficiency. The experimental results demonstrate that our method is considerably superior to recent image classification methods on this problem. The proposed methodology is able to deliver an end-to-end real-time car recognition system for video surveillance. Moreover, we have a tendency to construct a large and challenging car image knowledge set, consisting of thirty seven 195 real-world automobile images from thirty brands, which could serve as a customary benchmark during this field and be utilized in numerous related analysis communities.


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