JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method

Understand

Due to its variety of applications in the real-world, the task of single image-based crowd counting has received a lot of interest in the recent years.

• Recently, several approaches have been proposed to address various problems encountered in crowd counting.
• These approaches are essentially based on convolutional neural networks that require large amounts of data to train the network parameters.
• Considering this, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images with "1.51 million" annotations.