Publications

An unauthorized alteration in the viewpoint of a surveillance cameras is called tampering. We propose to employ a generative model that learns the distribution of images from the surveillance camera under normal operating conditions, by training a generative adversarial network (GAN). The GAN is capable of sampling images from the probability density function, which are used as reference. We train a Siamese network that transforms the images into a feature space, so as to maximize the distance between the generated images and tampered images (while minimizing the distance between generated and normal images). The distance between the generated and the surveillance camera image is classified as either normal or tampered.

P. Mantini and S.K. Shah

Video quality is an important practical challenge that is often overlooked in the design of automated video surveillance systems. Commonly, visual intelligent systems are trained and tested on high quality image datasets, yet in practical video surveillance applications the video frames cannot be assumed to be of high quality due to video encoding, transmission and decoding. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for object detection under various levels of video compression. We show that the existing detectors are susceptible to quality distortions stemming from compression artifacts during video acquisition. These results enable future work in developing object detectors that are more robust to video quality.

M. Aqqa, P. Mantini and S.K. Shah

Person re-identification is a challenge in video-based surveillance where the goal is to identify the same person in different camera views. In recent years, many algorithms have been proposed that approach this problem by designing suitable feature representations for images of persons or by training appropriate distance metrics that learn to distinguish between images of different persons. Aggregating the results from multiple algorithms for person re-identification is a relatively less-explored area of research. In this paper, we formulate an algorithm that maps the ranking process in a person re-identification algorithm to a problem in graph theory. We then extend this formulation to allow for the use of results from multiple algorithms to make a consensus-based decision for the person re-identification problem.

Arko Barman and Shishir K. Shah

IEEE International Conference on Computer Vision (ICCV), 1124-1133, (2017)

Camera tamper detection is the ability to detect faults and operational failures in video surveillance cameras by analyzing the video. Researchers have increasingly focused on such techniques attributing to the ubiquitous deployment of large scale surveillance systems. In this paper, a signal detection theory approach is proposed to quantitatively analyze the information being captured by the camera and to detect tampers. Signal activity is used as a feature to measure the amount of information in the image. The distribution of features representing the normal operation of a camera are modeled as a Gaussian mixture model (GMM). The GMM is trained using synthetic data. To reduce the effects of noise, a Kalman filter is used to model changes in signal activity in the video.

Pranav Mantini and Shishir K. Shah

IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 1-6, (2017)

Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction.

Pengfei Dou, Shishir K. Shah, and Ioannis A. Kakadiaris

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 1503-1512, (2017)