PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

May 24, 2019 · Entered Twilight · 🏛 IEEE transactions on circuits and systems for video technology (Print)

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Repo contents: .gitignore, README.md, __init__.py, config.py, datasets, imgs, loading_data.py, misc, models, test.py, train_lr.py

Authors Junyu Gao, Qi Wang, Xuelong Li arXiv ID 1905.10085 Category cs.CV: Computer Vision Cross-listed cs.AI Citations 248 Venue IEEE transactions on circuits and systems for video technology (Print) Repository https://github.com/gjy3035/PCC-Net ⭐ 600 Last Checked 1 month ago

Abstract

Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion. Many methods only focus on the local appearance features and they cannot handle the aforementioned challenges. In order to tackle them, we propose a Perspective Crowd Counting Network (PCC Net), which consists of three parts: 1) Density Map Estimation (DME) focuses on learning very local features for density map estimation; 2) Random High-level Density Classification (R-HDC) extracts global features to predict the coarse density labels of random patches in images; 3) Fore-/Background Segmentation (FBS) encodes mid-level features to segments the foreground and background. Besides, the DULR module is embedded in PCC Net to encode the perspective changes on four directions (Down, Up, Left and Right). The proposed PCC Net is verified on five mainstream datasets, which achieves the state-of-the-art performance on the one and attains the competitive results on the other four datasets. The source code is available at https://github.com/gjy3035/PCC-Net.