# Object Detection Through Exploration With A Foveated Visual Field

arXiv: https://arxiv.org/abs/1408.0814 · HTML: https://mlsanity.com/paper/1408.0814/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2014.

## Built on by (most cited first, 14 of 14)

- [FoveaTer: Foveated Transformer for Image Classification](https://mlsanity.com/paper/2105.14173/) (2021)
- [Target-absent Human Attention](https://mlsanity.com/paper/2207.01166/) (2022)
- [On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation](https://mlsanity.com/paper/2112.07173/) (2021)
- [A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains](https://mlsanity.com/paper/2310.13849/) (2023)
- [Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers](https://mlsanity.com/paper/2507.15833/) (2025)
- [LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models](https://mlsanity.com/paper/2603.14882/) (2026)
- [Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises](https://mlsanity.com/paper/2206.07282/) (2022)
- [HoughNet: Integrating near and long-range evidence for visual detection](https://mlsanity.com/paper/2104.06773/) (2021)
- [Learning to search for and detect objects in foveal images using deep learning](https://mlsanity.com/paper/2304.05741/) (2023)
- [The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions](https://mlsanity.com/paper/2107.06409/) (2021)
- [Policy-based Foveated Imaging and Perception](https://mlsanity.com/paper/2606.02565/) (2026)
- [Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2310.06578/) (2023)
- [FAVE: Foveated Adaptive Visual Encoding for Efficient Fine-Grained Visual Understanding](https://mlsanity.com/paper/2609.04392/) (2026)
- [Task-driven Processing with Coarse-to-Fine Glimpse-based Active Perception](https://mlsanity.com/paper/2609.09025/) (2026)

## Abstract (arXiv)

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.