  The paper presents a technique to improve human detection in still images
using deep learning. Our novel method, ViS-HuD, computes visual saliency map
from the image. Then the input image is multiplied by the map and product is
fed to the Convolutional Neural Network (CNN) which detects humans in the
image. A visual saliency map is generated using ML-Net and human detection is
carried out using DetectNet. ML-Net is pre-trained on SALICON while, DetectNet
is pre-trained on ImageNet database for visual saliency detection and image
classification respectively. The CNNs of ViS-HuD were trained on two
challenging databases - Penn Fudan and TUD-Brussels Benchmark. Experimental
results demonstrate that the proposed method achieves state-of-the-art
performance on Penn Fudan Dataset with 91.4% human detection accuracy and it
achieves average miss-rate of 53% on the TUDBrussels benchmark.
