arXiv Analytics

Sign in

arXiv:1805.08769 [cs.CV]AbstractReferencesReviewsResources

A Convolutional Feature Map based Deep Network targeted towards Traffic Detection and Classification

Baljit Kaur, Jhilik Bhattacharya

Published 2018-05-22Version 1

This research mainly emphasizes on traffic detection thus essentially involving object detection and classification. The particular work discussed here is motivated from unsatisfactory attempts of re-using well known pre-trained object detection networks for domain specific data. In this course, some trivial issues leading to prominent performance drop are identified and ways to resolve them are discussed. For example, some simple yet relevant tricks regarding data collection and sampling prove to be very beneficial. Also, introducing a blur net to deal with blurred real time data is another important factor promoting performance elevation. We further study the neural network design issues for beneficial object classification and involve shared, region-independent convolutional features. Adaptive learning rates to deal with saddle points are also investigated and an average covariance matrix based pre-conditioned approach is proposed. We also introduce the use of optical flow features to accommodate orientation information. Experimental results demonstrate that this results in a steady rise in the performance rate.

Related articles: Most relevant | Search more
arXiv:1803.01356 [cs.CV] (Published 2018-03-04)
Classification based Grasp Detection using Spatial Transformer Network
arXiv:1804.10167 [cs.CV] (Published 2018-04-26)
fMRI: preprocessing, classification and pattern recognition
arXiv:1712.06897 [cs.CV] (Published 2017-12-19)
Learning Fixation Point Strategy for Object Detection and Classification