# Generative Adversarial Models for People Attribute Recognition in Surveillance

arXiv: https://arxiv.org/abs/1707.02240 · HTML: https://mlsanity.com/paper/1707.02240/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2307.15252/) (2023)
- [Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Coding](https://mlsanity.com/paper/2106.15420/) (2021)
- [Pedestrian Attribute Recognition as Label-balanced Multi-label Learning](https://mlsanity.com/paper/2405.04858/) (2024)
- [A Data-Centric Approach to Pedestrian Attribute Recognition: Synthetic Augmentation via Prompt-driven Diffusion Models](https://mlsanity.com/paper/2509.02099/) (2025)
- [Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models](https://mlsanity.com/paper/2509.02161/) (2025)

## Abstract (arXiv)

In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing ...) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often occur in surveillance footages by enhancing the images by means of Deep Convolutional Generative Adversarial Networks (DCGAN). Experiments show that by combining both our Generative Reconstruction and Deep Attribute Classification Network we can effectively extract attributes even when resolution is poor and in presence of strong occlusions up to 80% of the whole person figure.