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