# Taking a Deeper Look at Pedestrians

arXiv: https://arxiv.org/abs/1501.05790 · HTML: https://mlsanity.com/paper/1501.05790/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2015.

## Built on by (most cited first, 15 of 15)

- [Weakly Aligned Feature Fusion for Multimodal Object Detection](https://mlsanity.com/paper/2204.09848/) (2022)
- [Towards Domain Generalization in Object Detection](https://mlsanity.com/paper/2203.14387/) (2022)
- [Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond](https://mlsanity.com/paper/2201.03176/) (2022)
- [PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning](https://mlsanity.com/paper/2310.09183/) (2023)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators](https://mlsanity.com/paper/2212.02649/) (2022)
- [Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning](https://mlsanity.com/paper/2408.03030/) (2024)
- [Personalized Federated Learning with Hidden Information on Personalized Prior](https://mlsanity.com/paper/2211.10684/) (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)
- [Segmentation-Based Bounding Box Generation for Omnidirectional Pedestrian Detection](https://mlsanity.com/paper/2104.13764/) (2021)
- [Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach](https://mlsanity.com/paper/2105.08253/) (2021)
- [CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection](https://mlsanity.com/paper/2212.05691/) (2022)
- [Continual Learning for Out-of-Distribution Pedestrian Detection](https://mlsanity.com/paper/2306.15117/) (2023)
- [Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator](https://mlsanity.com/paper/2404.09317/) (2024)

## Abstract (arXiv)

In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architectural choices, parameters, and the influence of different training data, including pre-training on surrogate tasks. We present the best convnet detectors on the Caltech and KITTI dataset. On Caltech our convnets reach top performance both for the Caltech1x and Caltech10x training setup. Using additional data at training time our strongest convnet model is competitive even to detectors that use additional data (optical flow) at test time.