CityPersons: A Diverse Dataset for Pedestrian Detection

Citations Over TimeTop 1% of 2017 papers

Abstract

Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data. We revisit CNN design and point out key adaptations, enabling plain FasterRCNN to obtain state-of-the-art results on the Caltech dataset. To achieve further improvement from more and better data, we introduce CityPersons, a new set of person annotations on top of the Cityscapes dataset. The diversity of CityPersons allows us for the first time to train one single CNN model that generalizes well over multiple benchmarks. Moreover, with additional training with CityPersons, we obtain top results using FasterRCNN on Caltech, improving especially for more difficult cases (heavy occlusion and small scale) and providing higher localization quality.

Related Papers

• → Improvement of Pedestrian Crossing Safety on Urban Roads(2010)8 cited
• → Pedestrian Conflicts, Pedestrian Comfort Levels, and Current Pedestrian Levels of Service(2013)2 cited
• → Review on Microscopic Pedestrian Simulation Model(2016)53 cited
• THE POSITION OF THE PEDESTRIAN ON CROSSINGS LOCATIONS. MAIN REPORT AND APPENDICES REPORT(1996)
• Analysis on Influence of Design of Pedestrian Bridge to Pedestrian Traffic(2013)