3-D Urban Objects Detection and Classification From Point Clouds

Alkhalili, Yassin; Luthra, Manisha; Rizk, Amr; Koldehofe, Boris

doi:10.1145/3328905.3330298

Cited by 3 publications

(2 citation statements)

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“…In such applications, radar is often used instead of or in addition to visual data [22]. Similarly, point clouds gathered using LIDAR are frequently used as in [37] and [3]. In this work, we focus on data generated by smartphones as explored in Section 4.…”

Section: Autonomous Driving Applicationsmentioning

confidence: 99%

Urban Object Detection Kit: A System for Collection and Analysis of Street-Level Imagery

Sukel

Rudinac

Worring

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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In this paper, we propose Urban Object Detection Kit, a system for the real-time collection and analysis of street-level imagery. The system is affordable and portable and allows local government agencies to receive actionable intelligence about the objects on the streets. This system can be attached to service vehicles, such as garbage trucks, parking scanners and maintenance cars, thus allowing for large-scale deployment. This will, in turn, result in street-level imagery captured at a high collection frequency, while covering a large geographical region. Unlike more traditional panoramic street-level imagery, the data collected by this system has a higher frequency, making it suitable for the highly dynamic nature of city streets. For example, the proposed system allows for real-time detection of urban objects and potential issues that require the attention of city services. It paves the way for easy deployment and testing of multimedia information retrieval algorithms in a dynamic realworld setting. We showcase the usefulness of object detection for identifying issues in public spaces that occur within a limited time span. Finally, we make the kit, as well as the data collected using it, openly available for the research community. CCS CONCEPTS• Computing methodologies → Visual inspection; Mobile agents; Distributed computing methodologies; • Information systems → Multimedia and multimodal retrieval.

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Section: Autonomous Driving Applicationsmentioning

confidence: 99%

Urban Object Detection Kit: A System for Collection and Analysis of Street-Level Imagery

Sukel

Rudinac

Worring

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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“…Prior existing Lidar-based surveillance solutions utilize mainly Rotating Multi-Beam (RMB) Lidar sensors (Alkhalili et al, 2019). These systems can capture point cloud sequences of the full 360 • view with a recording frequency of 15 − 30 fps.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Foreground Segmentation for Surveillance Applications in NRCS Lidar Sequences

Kovács

Kégl

Benedek

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, we propose a point-level foreground-background separation technique for the segmentation of measurement sequences of a Non-repetitive Circular Scanning (NRCS) Lidar sensor, which is used as a 3D surveillance camera mounted in a fixed position. We show that by applying the NRCS Lidar technology, we can overcome various limitations of rotating multi-beam Lidar sensors, such as low vertical measurement resolution, which is disadvantageous in surveillance applications. As the main challenge, we need to efficiently balance between the spatial and the temporal resolution of the recorded range data. For this reason, we automatically generate and maintain a very high-resolution background model of the sensor’s Field of View, while for enabling real-time analysis of dynamic objects we use low integration time to extract the consecutive time frames. As a result, the laser reflections from foreground objects reflect sparse, but geometrically accurate samples of the silhouettes providing valuable input for higher-level shape description or event analysis steps. We demonstrate the efficiency of the new approach in different realistic NRCS Lidar measurements sequences, obtaining a 0.76 overall F1-score on the measured dataset.

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