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Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
#3 best model for Crowd Counting on UCF-QNRF
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
#3 best model for Scene Segmentation on SUN-RGBD
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc.
This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F).
We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.
#3 best model for Crowd Counting on Venice
To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map.
Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.
Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds.
Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations.
#6 best model for Crowd Counting on UCF-QNRF