Pixel-wise Crowd Understanding via Synthetic Data

TL;DR

This paper introduces a synthetic crowd dataset generated from a video game and proposes methods to improve pixel-wise crowd understanding by leveraging synthetic data, enhancing performance in real-world scenarios.

Contribution

It creates a large-scale synthetic crowd dataset from a video game and proposes two methods—pre-training and domain adaptation—to improve real-world crowd analysis.

Findings

Synthetic data improves model performance on real data.

Pre-training on synthetic data enhances accuracy.

Domain adaptation yields photo-realistic training images.

Abstract

Crowd analysis via computer vision techniques is an important topic in the field of video surveillance, which has wide-spread applications including crowd monitoring, public safety, space design and so on. Pixel-wise crowd understanding is the most fundamental task in crowd analysis because of its finer results for video sequences or still images than other analysis tasks. Unfortunately, pixel-level understanding needs a large amount of labeled training data. Annotating them is an expensive work, which causes that current crowd datasets are small. As a result, most algorithms suffer from over-fitting to varying degrees. In this paper, take crowd counting and segmentation as examples from the pixel-wise crowd understanding, we attempt to remedy these problems from two aspects, namely data and methodology. Firstly, we develop a free data collector and labeler to generate synthetic and labeled crowd scenes in a computer game, Grand Theft Auto V. Then we use it to construct a large-scale, diverse synthetic crowd dataset, which is named as "GCC Dataset". Secondly, we propose two simple methods to improve the performance of crowd understanding via exploiting the synthetic data. To be specific, 1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; 2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels. As a result, the trained model works well in real crowd scenes.