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Posts
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Blog Post number 1
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publications
A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research
Published in 2022 IEEE Intelligent Vehicles Symposium (IV), 2022
Data-intensive machine learning based techniques increasingly play a prominent role in the development of future mobility solutions - from driver assistance and automation functions in vehicles, to real-time traffic management systems realized through dedicated infrastructure. The availability of high quality real-world data is often an important prerequisite for the development and reliable deployment of such systems in large scale. Towards this endeavour, we present the A9-Dataset based on roadside sensor infrastructure from the 3 km long Providentia++ test field near Munich in Germany. The dataset includes anonymized and precision-timestamped multi-modal sensor and object data in high resolution, covering a variety of traffic situations. As part of the first set of data, which we describe in this paper, we provide camera and LiDAR frames from two overhead gantry bridges on the A9 autobahn with the corresponding objects labeled with 3D bounding boxes. The first set includes in total more than 1000 sensor frames and 14000 traffic objects. The dataset is available for download at https://a9-dataset.com.
Systematic Error Source Analysis of a Real-World Multi-Camera Traffic Surveillance System
Published in 25th International Conference on Information Fusion (FUSION 2022), 2022
In this paper, we assess the performance of our real- world multi-camera traffic surveillance system along a segment of the A9 Autobahn north of Munich. Its principal component is a Labeled Multi-Bernoulli based tracking module that sequentially fuses the detection data from parallel camera processing pipelines. We present a systematic investigation of the system’s characteristic failure modes that lead to a degradation of its performance. To this end, we assess state of the art metrics and performance measures in regard to their suitability for flagging unwanted behavior or failures in real-world multi-object tracking systems. Our analysis is structured into three levels of abstraction: target-level, time-step-level, and track-level. These abstraction levels allow us to systematically approach the analysis from different perspectives and to direct the focus on recurring errors and systemic deficiencies. In particular, the track-level analysis proved to be the most expedient approach since it drew our attention to system challenges like occlusions and other time- correlated detection errors. It further identified the system bias introduced by the adoption of class-dependent object extents. Our analysis is intended to guide the future development effort of our system and to serve as a basis for investigations and improvements of similar systems.
Modeling Inter-Vehicle Occlusion Scenarios in Multi-Camera Traffic Surveillance Systems
Published in 26th International Conference on Information Fusion (FUSION 2023), 2023
In this paper, we present a novel design for a multi-camera tracking system with occlusion-handling capabilities and its application to a highway traffic surveillance system. The fundamental concept follows the tracking-by-detection principle with monocular detectors and an LMB tracker for tracking the objects in the world frame. All data from the multi-view setup is combined into one consistent representation of the real-time traffic situation. In order to assess the inter-target occlusion scenarios in 3D, the vehicles are modeled as cuboids and their extents are estimated from the bounding boxes provided by the detectors. We re-transform the 3D occlusion estimation problem into the 2D camera space and present two methods for quantifying the occlusion state of the objects. Moreover, we propose a modification to the computation of the existence probability of undetected and occluded targets. Based on this, the tracking system is extended by an occlusion-aware detection model. We evaluate our occlusion-handling approach on a real-world traffic dataset from the Providentia++ project and show an improved tracking performance. We find that the number of misdetected targets is reduced and more track identities are preserved.
An Online Self-Correcting Calibration Architecture for Multi-Camera Traffic Localization Infrastructure
Accepted for 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
Most vision-based sensing and localization infras- tructure today employ conventional area scanning cameras due to the high information density and cost efficiency offered by them. While the information-rich two-dimensional images provided by such sensors make it easier to detect and classify traffic objects with the help of deep neural networks, their accurate localization in the three-dimensional real world also calls for a reliable calibration methodology, that maintains accuracy not just during installation, but also under continuous operation over time. In this paper, we propose a camera calibration architecture that extracts and uses corresponding targets from high definition maps, augment it with an efficient stabilization mechanism in order to compensate for the errors arising out of fast transient vibrations and slow orientational drifts. Finally, we evaluate its performance on a real-world test site.
Joint Vehicle Pose and Extent Estimation in the Context of Multi-Camera Traffic Surveillance
Accepted for 27th International Conference on Information Fusion (FUSION 2024), 2024
In this paper, we introduce a novel method for the estimation of vehicle pose and extent in traffic surveillance scenarios based on camera data. The state estimation is performed in a common world frame, enabling the seamless integration of the image data from different viewpoints. Our approach incorporates the non-linear transformation between the measurements and the states directly into the framework of an Unscented Kalman filter. Two measurement models are proposed: one designed for bounding boxes and another for discretized object contours extracted from segmentation masks. The method is evaluated using data from a real-world traffic surveillance system, demonstrating the high effectiveness and good feasibility of our approach for localizing passing cars.
talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
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