# Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images

arXiv: https://arxiv.org/abs/1803.06077 · HTML: https://mlsanity.com/paper/1803.06077/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

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

- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [Convolution kernel adaptation to calibrated fisheye](https://mlsanity.com/paper/2402.01456/) (2024)
- [Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras](https://mlsanity.com/paper/2107.08862/) (2021)
- [Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework](https://mlsanity.com/paper/2212.04111/) (2022)
- [LineMarkNet: Line Landmark Detection for Valet Parking](https://mlsanity.com/paper/2309.10475/) (2023)
- [PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving](https://mlsanity.com/paper/2309.11002/) (2023)

## Abstract (arXiv)

The ability to detect pedestrians and other moving objects is crucial for an autonomous vehicle. This must be done in real-time with minimum system overhead. This paper discusses the implementation of a surround view system to identify moving as well as static objects that are close to the ego vehicle. The algorithm works on 4 views captured by fisheye cameras which are merged into a single frame. The moving object detection and tracking solution uses minimal system overhead to isolate regions of interest (ROIs) containing moving objects. These ROIs are then analyzed using a deep neural network (DNN) to categorize the moving object. With deployment and testing on a real car in urban environments, we have demonstrated the practical feasibility of the solution. The video demos of our algorithm have been uploaded to Youtube: https://youtu.be/vpoCfC724iA, https://youtu.be/2X4aqH2bMBs