Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images. The proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN)... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Crowd Counting ShanghaiTech A CP-CNN MAE 73.6 # 9
Crowd Counting ShanghaiTech B CP-CNN MAE 20.1 # 12
Crowd Counting UCF CC 50 CP-CNN MAE 295.8 # 10
Crowd Counting WorldExpo’10 CP-CNN Average MAE 8.9 # 6

Methods used in the Paper


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