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Crowd Counting On Worldexpo10

Metrics

Average MAE

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Zhang et al.	12.9	Cross-Scene Crowd Counting via Deep Convolutional Neural Networks	-
MCNN	11.6	Single-Image Crowd Counting via Multi-Column Convolutional Neural Network	-
IG-CNN	11.3	Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN	-
ic-CNN	10.3	Iterative Crowd Counting	-
Switch-CNN	9.4	Switching Convolutional Neural Network for Crowd Counting
DecideNet	9.23	DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
D-ConvNet	9.1	Crowd Counting With Deep Negative Correlation Learning	-
CP-CNN	8.9	Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs	-
CSRNet	8.6	CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
SANet	8.2	Scale Aggregation Network for Accurate and Efficient Crowd Counting	-
SPANet	7.7	Learning Spatial Awareness to Improve Crowd Counting	-
ACSCP	7.5	Crowd Counting via Adversarial Cross-Scale Consistency Pursuit	-
CAN	7.4	Context-Aware Crowd Counting
M-SFANet	7.32	Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
ECAN	7.2	Context-Aware Crowd Counting

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Crowd Counting On Worldexpo10 | SOTA | HyperAI