UHCTD: A Comprehensive Dataset for Camera Tampering Detection

Abstract

An unauthorized or an accidental change in the view of a surveillance camera is called a tampering. Algorithms that detect tampering by analyzing the video are referred to as camera tampering detection algorithms. Most evaluations on camera tampering detection methods are presented based on individually collected datasets. One of the major challenges in the area of camera tamper detection is the absence of a public dataset with sufficient size and variations for an extensive performance evaluation. We propose a large scale synthetic dataset called University of Houston Camera Tampering Detection dataset (UHCTD) for development and testing of camera tampering detection methods. The dataset consists of a total 576 tampers with over 288 hours of video captured from two surveillance cameras. To establish an initial benchmark, we cast camera tampering detection as a classification problem. We train and evaluate three different deep architectures that have shown promise in scene classification, Alexnet, Resnet, and Densenet. Results are presented to show how the dataset can be used to train and classify images as normal, and tampered within and across cameras.