# Evolving Boxes for Fast Vehicle Detection

arXiv: https://arxiv.org/abs/1702.00254 · HTML: https://mlsanity.com/paper/1702.00254/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2017.

## Built on by (most cited first, 6 of 6)

- [Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.16470/) (2021)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Extract-Transform-Load for Video Streams](https://mlsanity.com/paper/2310.04830/) (2023)
- [FFAVOD: Feature Fusion Architecture for Video Object Detection](https://mlsanity.com/paper/2109.07298/) (2021)
- [Transformer-based assignment decision network for multiple object tracking](https://mlsanity.com/paper/2208.03571/) (2022)
- [Localization-Based Tracking](https://mlsanity.com/paper/2104.05823/) (2021)

## Abstract (arXiv)

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9-13 FPS detection speed on a moderate commercial GPU.