A Survey of Feature Types and Their Contributions for Camera Tampering Detection
This is an incremental survey and experimental analysis for surveillance security applications.
The paper tackles camera tampering detection by reviewing feature types and evaluating their performance through time series analysis on real-world surveillance video, quantifying model results with specific metrics.
Camera tamper detection is the ability to detect unauthorized and unintentional alterations in surveillance cameras by analyzing the video. Camera tampering can occur due to natural events or it can be caused intentionally to disrupt surveillance. We cast tampering detection as a change detection problem, and perform a review of the existing literature with emphasis on feature types. We formulate tampering detection as a time series analysis problem, and design experiments to study the robustness and capability of various feature types. We compute ten features on real-world surveillance video and apply time series analysis to ascertain their predictability, and their capability to detect tampering. Finally, we quantify the performance of various time series models using each feature type to detect tampering.