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Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations by multiple partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood for multiple humans based on the appearance of the humans, the visibility of the body obtained by occlusion reasoning,...
This paper presents a detection based object tracking method that forms object trajectories by associating detection responses. Discriminative classifiers of objects of a known class are learned and applied to the video sequence frame by frame. The output of the detection module is a "soft decision", which consists of a set of detection responses of different confidence levels. Responses...
Due to increased interest in visual surveillance, various multiple object tracking methods have been recently proposed and applied to pedestrian tracking. However in presence of intensive inter-object occlusion and sensor gaps, most of these methods result in tracking failures. We present a two-stage multi-object tracking approach to robustly track pedestrians in such complex scenarios. We first generate...
In this paper, we present a probabilistic framework for automatic detection and tracking of objects. We address the data association problem by formulating the visual tracking as finding the best partition of a measurement graph containing all detected moving regions. In order to incorporate model information in tracking procedure, the posterior distribution is augmented with Adaboost image likelihood...
This paper presents a novel robust sliding mode controller with fuzzy tuning for Stewart platform servo system, which suffers from high nonlinear dynamics and serious load coupling among channels. The controller consists of an equivalent control to assign desired dynamics to the closed-loop system, a switching control to guarantee a sliding mode, and a fuzzy control to enhance fast tracking and attenuate...
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