# Scale-aware Fast R-CNN for Pedestrian Detection

arXiv: https://arxiv.org/abs/1510.08160 · HTML: https://mlsanity.com/paper/1510.08160/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2015.

## Built on by (most cited first, 30 of 32)

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- [Pedestrian Detection by Exemplar-Guided Contrastive Learning](https://mlsanity.com/paper/2111.08974/) (2021)
- [Radio-Assisted Human Detection](https://mlsanity.com/paper/2112.08743/) (2021)
- [Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning](https://mlsanity.com/paper/2408.03030/) (2024)
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- [ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection](https://mlsanity.com/paper/2210.04574/) (2022)
- [Imagine the Unseen: Occluded Pedestrian Detection via Adversarial Feature Completion](https://mlsanity.com/paper/2405.01311/) (2024)
- [Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection](https://mlsanity.com/paper/2103.10091/) (2021)
- [Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset](https://mlsanity.com/paper/2112.02221/) (2021)
- [CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection](https://mlsanity.com/paper/2212.05691/) (2022)
- [Rt-Track: Robust Tricks for Multi-Pedestrian Tracking](https://mlsanity.com/paper/2303.09668/) (2023)
- [Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems](https://mlsanity.com/paper/2306.01282/) (2023)
- [PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving](https://mlsanity.com/paper/2309.11002/) (2023)

## Abstract (arXiv)

In this work, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically different features. Thus, large variance in instance scales, which results in undesirable large intra-category variance in features, may severely hurt the performance of modern object instance detection methods. We argue that this issue can be substantially alleviated by the divide-and-conquer philosophy. Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework. The model introduces multiple built-in sub-networks which detect pedestrians with scales from disjoint ranges. Outputs from all the sub-networks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over the sizes of object proposals. Extensive evaluations on several challenging pedestrian detection datasets well demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our method achieves state-of-the-art performance on Caltech, INRIA, and ETH, and obtains competitive results on KITTI.