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

Source: [Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties](<https://www.sota2.com/research/paper/68fa64411e209131cea037fe>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-05T05:01:30.457Z

Authors: Achim Kampker, Mohsen Sefati, Arya Abdul Rachman, Kai Kreisk\\"other, Pascual Campoy

Published: 2018-01-08T21:05:10.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1801.02686>)

[PDF](<https://arxiv.org/pdf/1801.02686.pdf>)