{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/temporal-localization-of-non-static-digital","title":"Temporal Localization of Non-Static Digital Videos Using the Electrical Network Frequency","arxiv_id":null,"date":"2020-04-20","proceeding":"IEEE Signal Processing Letters 2020 4","authors":["Samuel Fernández-Menduiña","Fernando Pérez-González"],"abstract":"Non-static scenes represent one of the main barriers for retrieving the electrical network frequency (ENF) from digital videos recorded in realistic scenarios, since movement influences the luminance of pixels, hindering the recovery of the desired information. Aiming to mitigate the effects of changes in the scene, in this letter a video processing stage, that detects and combines those pixels that are not affected by movement, is proposed. The sequence obtained thereby is delivered to a PLL-based FM demodulator, that estimates the ENF taking into account its autoregressive nature. Additionally, a frame rate estimation algorithm is employed to avoid time-consuming trial and error loops in the extraction process. The performance of the system is tested by computing the probability of estimating correctly the time of recording of a given video, using ground-truth sequences obtained directly from the mains or a reliable database.","url_abs":"https://ieeexplore.ieee.org/document/9072308","url_pdf":"https://ieeexplore.ieee.org/document/9072308","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"enf-electric-network-frequency-extraction-1","task_name":"ENF (Electric Network Frequency) Extraction from Video"},{"task_slug":"temporal-localization","task_name":"Temporal Localization"}],"methods":[],"datasets_introduced":[{"slug":"enf-moving-video","name":"ENF moving video","full_name":"Electric Network Frequency Moving Video Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}