Presentation + Paper
21 October 2016 Automated object detection and tracking with a flash LiDAR system
Author Affiliations +
Proceedings Volume 9988, Electro-Optical Remote Sensing X; 998803 (2016) https://doi.org/10.1117/12.2240640
Event: SPIE Security + Defence, 2016, Edinburgh, United Kingdom
Abstract
The detection of objects, or persons, is a common task in the fields of environment surveillance, object observation or danger defense. There are several approaches for automated detection with conventional imaging sensors as well as with LiDAR sensors, but for the latter the real-time detection is hampered by the scanning character and therefore by the data distortion of most LiDAR systems. The paper presents a solution for real-time data acquisition of a flash LiDAR sensor with synchronous raw data analysis, point cloud calculation, object detection, calculation of the next best view and steering of the pan-tilt head of the sensor. As a result the attention is always focused on the object, independent of the behavior of the object. Even for highly volatile and rapid changes in the direction of motion the object is kept in the field of view. The experimental setup used in this paper is realized with an elementary person detection algorithm in medium distances (20 m to 60 m) to show the efficiency of the system for objects with a high angular speed. It is easy to replace the detection part by any other object detection algorithm and thus it is easy to track nearly any object, for example a car or a boat or an UAV in various distances.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marcus Hammer, Marcus Hebel, and Michael Arens "Automated object detection and tracking with a flash LiDAR system", Proc. SPIE 9988, Electro-Optical Remote Sensing X, 998803 (21 October 2016); https://doi.org/10.1117/12.2240640
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CITATIONS
Cited by 7 scholarly publications.
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KEYWORDS
LIDAR

Sensors

Clouds

Detection and tracking algorithms

Data conversion

Target detection

Head

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