CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

CVPR 2018 • Yuhong Li • Xiaofan Zhang • Deming Chen

We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Crowd Counting ShanghaiTech A CSRNet MAE 68.2 # 5
Crowd Counting ShanghaiTech B CSRNet MAE 10.6 # 5
Crowd Counting UCF CC 50 CSRNet MAE 266.1 # 6
Crowd Counting WorldExpo’10 CSRNet Average MAE 8.6 # 5

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Crowd Counting Venice CSRNet MAE 35.8 # 3