2,935 research outputs found
Focus for Free in Density-Based Counting
This work considers supervised learning to count from images and their
corresponding point annotations. Where density-based counting methods typically
use the point annotations only to create Gaussian-density maps, which act as
the supervision signal, the starting point of this work is that point
annotations have counting potential beyond density map generation. We introduce
two methods that repurpose the available point annotations to enhance counting
performance. The first is a counting-specific augmentation that leverages point
annotations to simulate occluded objects in both input and density images to
enhance the network's robustness to occlusions. The second method, foreground
distillation, generates foreground masks from the point annotations, from which
we train an auxiliary network on images with blacked-out backgrounds. By doing
so, it learns to extract foreground counting knowledge without interference
from the background. These methods can be seamlessly integrated with existing
counting advances and are adaptable to different loss functions. We demonstrate
complementary effects of the approaches, allowing us to achieve robust counting
results even in challenging scenarios such as background clutter, occlusion,
and varying crowd densities. Our proposed approach achieves strong counting
results on multiple datasets, including ShanghaiTech Part\_A and Part\_B,
UCF\_QNRF, JHU-Crowd++, and NWPU-Crowd.Comment: 18 page
PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos
Traditional crowd counting (optical flow or feature matching) techniques have been upgraded to deep learning (DL) models due to their lack of automatic feature extraction and low-precision outcomes. Most of these models were tested on surveillance scene crowd datasets captured by stationary shooting equipment. It is very challenging to perform people counting from the videos shot with a head-mounted moving camera; this is mainly due to mixing the temporal information of the moving crowd with the induced camera motion. This study proposed a transfer learning-based PeopleNet model to tackle this significant problem. For this, we have made some significant changes to the standard VGG16 model, by disabling top convolutional blocks and replacing its standard fully connected layers with some new fully connected and dense layers. The strong transfer learning capability of the VGG16 network yields in-depth insights of the PeopleNet into the good quality of density maps resulting in highly accurate crowd estimation. The performance of the proposed model has been tested over a self-generated image database prepared from moving camera video clips, as there is no public and benchmark dataset for this work. The proposed framework has given promising results on various crowd categories such as dense, sparse, average, etc. To ensure versatility, we have done self and cross-evaluation on various crowd counting models and datasets, which proves the importance of the PeopleNet model in adverse defense of society
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