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The paper presents a technique to improve human detection in still imagesusing deep learning. Our novel method, ViS-HuD, computes visual saliency mapfrom the image. Then the input image is multiplied by the map and product isfed to the Convolutional Neural Network (CNN) which detects humans in theimage. A visual saliency map is generated using ML-Net and human detection iscarried out using DetectNet. ML-Net is pre-trained on SALICON while, DetectNetis pre-trained on ImageNet database for visual saliency detection and imageclassification respectively. The CNNs of ViS-HuD were trained on twochallenging databases - Penn Fudan and TUD-Brussels Benchmark. Experimentalresults demonstrate that the proposed method achieves state-of-the-artperformance on Penn Fudan Dataset with 91.4% human detection accuracy and itachieves average miss-rate of 53% on the TUDBrussels benchmark.

ViS-HuD: Using Visual Saliency to Improve Human Detection with Convolutional Neural Networks