Using sensor noise to identify low resolution compressed videos from YouTube

The Photo Response Non-Uniformity acts as a digital fingerprint that can be used to identify image sensors. This characteristic has been used in previous research to identify scanners, digital photo cameras and digital video cameras. In this paper we use a wavelet filter from Lukáˇs et al [1] to extract the PRNU patterns from multiply compressed low resolution video files originating from webcameras after they have been uploaded to YouTube. The video files were recorded with various resolutions, and the resulting video files were encoded with different codecs. Depending on video characteristics (e.g. codec quality settings, recording resolution), it is possible to correctly identify cameras based on these videos

Graph-based video sequence matching using dominant colour graph profile (DCGP)

This paper presents a fast and effective technique for videos’ visual similarity detection and measurement using compact fixed-length signatures. The proposed technique (dominant colour graph profile DCGP) extracts and encodes the spatio-temporal information of a given video shot into a graph-based structure (tree) that fully captures this vital information. The graph structured properties are utilized to construct a fixed-length video signature of 112 decimal values per video shot. The encoded spatio-temporal information is extracted following channelling each video frame into a block-based structure, where the positions of respective blocks are tracked across video frames and encoded into multiple DCGP trees. The proposed technique provides a high matching speed (>2000 fps) and robust retrieval performance. The experiments on various standard and challenging datasets shows the framework’s robust performance, in terms of both, retrieval and computational performances

Human Behavior Understanding for Robotics

Abstract. Human behavior is complex, but structured along individual and social lines. Robotic systems interacting with people in uncontrolled environments need capabilities to correctly interpret, predict and respond to human behaviors. This paper discusses the scientific, technological and application challenges that arise from the mutual interaction of robotics and computational human behavior understanding. We supply a short survey of the area to provide a contextual framework and describe the most recent research in this area.