Brightness correction and shadow removal for video change detection with UAVs

Thomas Müller; Bastian Erdnüeß

doi:10.1117/12.2518618

2 May 2019 Brightness correction and shadow removal for video change detection with UAVs

Thomas Müller, Bastian Erdnüeß

Proceedings Volume 11009, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019; 1100906 (2019) https://doi.org/10.1117/12.2518618
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Unmanned aerial vehicles (UAVs) on patrol that recognize scene changes are a powerful instrument in a lot of surveillance and reconnaissance applications, e.g., protection of convoys from improvised explosive devices (IEDs) by early detecting deployment traces, intruder or manipulation detection for real estates and military camps, or danger recognition and assessment in disaster monitoring. For that purpose, a visual-optical camera records video streams in which an automatic video change detection computes relevant or suspicious changes between two patrols. The main challenge is to identify true scene changes, i.e. to filter out changes caused by camera brightness variations, illumination changes due to cloudiness or time of day, or varying shadows cast by clouds or scene objects. Different partly overlapping or orthogonal solutions for all that, like chromatic adaptation, local mean adjustment, retinex, intensity equalization, and CIE Lab shadow removal, are evaluated and advantages/disadvantages are compared. Finally, the optimal method selection and combination is determined in order to maximize the change detection performance. Depicted results document the reached progress.

Conference Presentation

Citation Download Citation

Thomas Müller and Bastian Erdnüeß "Brightness correction and shadow removal for video change detection with UAVs", Proc. SPIE 11009, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019, 1100906 (2 May 2019); https://doi.org/10.1117/12.2518618

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