# Vehicle Tracking Using Surveillance with Multimodal Data Fusion

arXiv: https://arxiv.org/abs/1811.02627 · HTML: https://mlsanity.com/paper/1811.02627/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search](https://mlsanity.com/paper/2403.15712/) (2024)
- [ControlFusion: A Controllable Image Fusion Framework with Language-Vision Degradation Prompts](https://mlsanity.com/paper/2503.23356/) (2025)
- [VideoFusion: A Spatio-Temporal Collaborative Network for Multi-modal Video Fusion](https://mlsanity.com/paper/2503.23359/) (2025)
- [Bangladeshi Native Vehicle Detection in Wild](https://mlsanity.com/paper/2405.12150/) (2024)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [MdaIF: Robust One-Stop Multi-Degradation-Aware Image Fusion with Language-Driven Semantics](https://mlsanity.com/paper/2511.12525/) (2025)
- [Unleashing the Power of Text: Text-Guided Flow Matching for Image Fusion under Complex Degradations](https://mlsanity.com/paper/2608.00530/) (2026)

## Abstract (arXiv)

Vehicle location prediction or vehicle tracking is a significant topic within connected vehicles. This task, however, is difficult if only a single modal data is available, probably causing bias and impeding the accuracy. With the development of sensor networks in connected vehicles, multimodal data are becoming accessible. Therefore, we propose a framework for vehicle tracking with multimodal data fusion. Specifically, we fuse the results of two modalities, images and velocity, in our vehicle-tracking task. Images, being processed in the module of vehicle detection, provide direct information about the features of vehicles, whereas velocity estimation can further evaluate the possible location of the target vehicles, which reduces the number of features being compared, and decreases the time consumption and computational cost. Vehicle detection is designed with a color-faster R-CNN, which takes both the shape and color of the vehicles into consideration. Meanwhile, velocity estimation is through the Kalman filter, which is a classical method for tracking. Finally, a multimodal data fusion method is applied to integrate these outcomes so that vehicle-tracking tasks can be achieved. Experimental results suggest the efficiency of our methods, which can track vehicles using a series of surveillance cameras in urban areas.