Crowd Counting On Ucf Cc 50

Metrics

MAE

Results

Performance results of various models on this benchmark

		Paper Title
Zhang et al.	467.0	Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
Idrees et al.	419.5	Multi-source Multi-scale Counting in Extremely Dense Crowd Images
MCNN	377.6	Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Liu et al.	337.6	Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
Cascaded-MTL	322.8	CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
Switch-CNN	318.1	Switching Convolutional Neural Network for Crowd Counting
CP-CNN	295.8	Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
IG-CNN	291.4	Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN
ACSCP	291.0	Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
D-ConvNet	288.4	Crowd Counting With Deep Negative Correlation Learning
CSRNet	266.1	CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
ic-CNN	260.9	Iterative Crowd Counting
SANet	258.4	Scale Aggregation Network for Accurate and Efficient Crowd Counting
SPANet	232.6	Learning Spatial Awareness to Improve Crowd Counting
LSC-CNN	225.6	Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection
SGANet	224.6	Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
SGANet + CL	221.9	Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
CAN	212.2	Context-Aware Crowd Counting
DM-Count	211.0	Distribution Matching for Crowd Counting
GauNet (ResNet-50)	186.3	Rethinking Spatial Invariance of Convolutional Networks for Object Counting

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Crowd Counting On Ucf Cc 50

Metrics

Results