X-Tracking: Tracking Human in Masking Surveillance Video

Zewei Wu, Wei Ke, Cui Wang, Zhang Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Pedestrian tracking studies have been facilitated by a large amount of surveillance apparatus in the city while also raising public privacy concerns. In this paper, we propose X-Tracking, a privacy-aware pedestrian tracking paradigm designed for vision systems in Smart City. It allows low-cost compatibility with existing surveillance architecture. To protect entities' privacy, X-Tracking uses video pre-processing with desensitization so that identity information is unexposed to the tracking algorithm. We implement system-level privacy protection by redesigning the tracking framework that decouples all services based on a single responsibility principle. Then, we elaborate on the roles, behaviors, and protocols used in the new system and illustrate how the paradigm strikes a favorable balance between privacy protection and convenience services. Furthermore, we propose a new tracking task that aims to track humans in masking surveillance video. It is comparable to previous tracking tasks but considering the target with a distorted appearance poses new challenges for visual tracking. Finally, we evaluate the baseline algorithm on the task with a demo dataset.

Original languageEnglish
Title of host publication6th IEEE International Conference on Universal Village, UV 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665474771
Publication statusPublished - 2022
Event6th IEEE International Conference on Universal Village, UV 2022 - Hybrid, Boston, United States
Duration: 22 Oct 202225 Oct 2022

Publication series

Name6th IEEE International Conference on Universal Village, UV 2022


Conference6th IEEE International Conference on Universal Village, UV 2022
Country/TerritoryUnited States
CityHybrid, Boston


  • city surveillance
  • masking tracking
  • multiple objective tracking
  • pedestrian tracking
  • privacy preserving


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