Murtaza Taj, Andrea Cavallaro International Conference on Image Processing, ICIP 2012 Abstract We present an event recognition framework to detect interactions among objects, for example people, using a network of cameras and associated microphone pairs. The complementarity of the video and audio modalities is exploited to cover wide areas. In particular, object movements in portions of the scene that…
Murtaza Taj, Andrea Cavallaro IEEE Signal Processing Magazine, 2011 Abstract In recent years the decreasing cost of cameras and advances in miniaturization have favoured the deployment of largescale camera networks. This growing number of cameras enables new signal processing applications that use cooperatively multiple sensors over wide areas. In particular, object tracking is an important…
Fahad Daniyal, Murtaza Taj, Andrea Cavallaro Multimedia Tools and Applications, 2009 Abstract We present a content-aware multi-camera selection technique that uses object and frame-level features. First objects are detected using a color-based change detector. Next trajectory information for each object is generated using multi-frame graph matching. Finally, multiple features including size and location are used…
Huiyu Zhou, Murtaza Taj, Andrea Cavallaro IEEE Journal of Selected Topics in Signal Processing, 2008 Abstract We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on Time Difference of Arrival (TDOA) estimation between…
Emilio Maggio, Murtaza Taj, Andrea Cavallaro IEEE Transaction on Circuits System and Video Technology, 2008 Abstract We propose a filtering framework for multi-target tracking that is based on the Probability Hypothesis Density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of…
Stefan Karlsson, Murtaza Taj, Andrea Cavallaro EURASIP Journal on Image and Video Processing, 2008 Abstract We present a video analysis framework that integrates prior knowledge in object tracking to automatically detect humans and faces, and can be used to generate abstract representations of video (key-objects and object trajectories). The analysis framework is based on the fusion…
