# Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties

arXiv: https://arxiv.org/abs/1801.02686 · HTML: https://mlsanity.com/paper/1801.02686/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR](https://mlsanity.com/paper/2202.13505/) (2022)
- [Tiny Neural Networks for Multi-Object Tracking in a Modular Kalman Framework](https://mlsanity.com/paper/2504.02519/) (2025)

## Abstract (arXiv)

Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance the vehicle perception. We present a real-time integrated framework of multi-target object detection and tracking using 3D LIDAR geared toward urban use. Our approach combines sensor occlusion-aware detection method with computationally efficient heuristics rule-based filtering and adaptive probabilistic tracking to handle uncertainties arising from sensing limitation of 3D LIDAR and complexity of the target object movement. The evaluation results using real-world pre-recorded 3D LIDAR data and comparison with state-of-the-art works shows that our framework is capable of achieving promising tracking performance in the urban situation.