Tracking Across Multiple Moving Airborne Cameras
Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this work, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a non-parametric density estimation method over a joint domain-range representation of image pixels, multi-modal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.
Yaser Sheikh and Mubarak Shah, Object Tracking Across Multiple Independently Moving Airborne Cameras, IEEE ICCV 2005, Beijing, China, October 15-21.
Yaser Sheikh, Alexei Gritai, Imran Junejo, Robert Muise, Abhijit Mahalanobis, and Mubarak Shah, Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras, SPIE Symposium on Defense and Security, 2005.