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Although recent advances in regional Convolutional Neural Networks (CNNs)enable them to outperform conventional techniques on standard object detectionand classification tasks, their response time is still slow for real-timeperformance. To address this issue, we propose a method for region proposal asan alternative to selective search, which is used in current state-of-the artobject detection algorithms. We evaluate our Keypoint Density-based RegionProposal (KDRP) approach and show that it speeds up detection andclassification on fine-grained tasks by 100% versus the existing selectivesearch region proposal technique without compromising classification accuracy.KDRP makes the application of CNNs to real-time detection and classificationfeasible.

Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network Features