most citedMulti-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting

8 citations · 23 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20208 cited

Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting

Usman Sajid, Wenchi Ma, Guanghui Wang

Crowd counting in still images is a challenging problem in practice due to huge crowd-density variations, large perspective changes, severe occlusion, and variable lighting conditi…

cs.CV20201 cited

Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks

Brian McClannahan, Krushi Patel, Usman Sajid +2

The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences. To this end, we first propose an efficient approach to convert t…

cs.CV20202 cited

ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images

Usman Sajid, Hasan Sajid, Hongcheng Wang +1

This paper proposes a novel approach for crowd counting in low to high density scenarios in static images. Current approaches cannot handle huge crowd diversity well and thus perfo…

cs.CV20207 cited

Plug-and-Play Rescaling Based Crowd Counting in Static Images

Usman Sajid, Guanghui Wang

Crowd counting is a challenging problem especially in the presence of huge crowd diversity across images and complex cluttered crowd-like background regions, where most previous ap…

cs.CV20195 cited

Object Detection with Convolutional Neural Networks

Kaidong Li, Wenchi Ma, Usman Sajid +2

In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN). Several classical CNN-based detectors are pres…