# Let's Roll: Synthetic Dataset Analysis for Pedestrian Detection Across Different Shutter Types

arXiv: https://arxiv.org/abs/2309.08136 · HTML: https://mlsanity.com/paper/2309.08136/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Rolling-Shutter-Aware Differential SfM and Image Rectification](https://mlsanity.com/paper/1903.03943/) (2019)
- [Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry](https://mlsanity.com/paper/1605.00475/) (2016)
- [SUNet: Symmetric Undistortion Network for Rolling Shutter Correction](https://mlsanity.com/paper/2108.04775/) (2021)
- [P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications](https://mlsanity.com/paper/2203.04737/) (2022)
- [Rolling-Shutter Modelling for Direct Visual-Inertial Odometry](https://mlsanity.com/paper/1911.01015/) (2019)
- [Learning Adaptive Warping for Real-World Rolling Shutter Correction](https://mlsanity.com/paper/2204.13886/) (2022)
- [P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking](https://mlsanity.com/paper/2205.14285/) (2022)
- [In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision](https://mlsanity.com/paper/2212.10881/) (2022)
- [Inertial-aided Rolling Shutter Relative Pose Estimation](https://mlsanity.com/paper/1712.00184/) (2017)
- [ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras](https://mlsanity.com/paper/2305.18371/) (2023)
- [IMU-Assisted Learning of Single-View Rolling Shutter Correction](https://mlsanity.com/paper/2011.03106/) (2020)

## Built on by (most cited first, 1 of 1)

- [Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes](https://mlsanity.com/paper/2409.03022/) (2024)

## Abstract (arXiv)

Computer vision (CV) pipelines are typically evaluated on datasets processed by image signal processing (ISP) pipelines even though, for resource-constrained applications, an important research goal is to avoid as many ISP steps as possible. In particular, most CV datasets consist of global shutter (GS) images even though most cameras today use a rolling shutter (RS). This paper studies the impact of different shutter mechanisms on machine learning (ML) object detection models on a synthetic dataset that we generate using the advanced simulation capabilities of Unreal Engine 5 (UE5). In particular, we train and evaluate mainstream detection models with our synthetically-generated paired GS and RS datasets to ascertain whether there exists a significant difference in detection accuracy between these two shutter modalities, especially when capturing low-speed objects (e.g., pedestrians). The results of this emulation framework indicate the performance between them are remarkably congruent for coarse-grained detection (mean average precision (mAP) for IOU=0.5), but have significant differences for fine-grained measures of detection accuracy (mAP for IOU=0.5:0.95). This implies that ML pipelines might not need explicit correction for RS for many object detection applications, but mitigating RS effects in ISP-less ML pipelines that target fine-grained location of the objects may need additional research.