Alexandros Makris

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In this paper we present our research towards the detection of violent scenes in movies, employing fusion methodologies, based on learning. Towards this goal, a multi-step approach is followed: initially, automated auditory and visual processing and analysis is performed in order to estimate probabilistic measures regarding particular audio and visual(More)
This paper presents a Bayesian model for the multiple-target tracking problem that handles a varying number of splitting and merging targets applied to convective cloud tracking. The model decomposes the tracking solution into events and target states. The events include target births, deaths, splits, and merges. The target states contain both the target(More)
Perception is a key component for any robotic system. In this paper we present a method to construct occupancy grids by fusing sensory information using Linear Opinion Pools. We used lidar sensors and a stereo-vision system mounted on a vehicle to make the experiments. To perform the validation, we compared the proposed method with the fusion method(More)
a r t i c l e i n f o A Hierarchical Model Fusion (HMF) framework for object tracking in video sequences is presented. The Bayesian tracking equations are extended to account for multiple object models. With these equations as a basis a particle filter algorithm is developed to efficiently cope with the multi-modal distributions emerging from cluttered(More)
In this paper, an object class recognition method is presented. The method uses local image features and follows the part-based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given distance. To train the system for an(More)
Sequential and variational assimilation methods allow tracking physical states using dynamic prior together with external observation of the studied system. However, when dense image satellite observations are available, such approaches realize a correction of the amplitude of the different state values but do not incorporate the spatial errors of structure(More)
A new method for object tracking in video sequences is presented. This method exploits the benefits of particle filters to tackle the multimodal distributions emerging from cluttered scenes. The tracked object is described by several models of different complexity, which are probabilistically linked together. The parameter update for each model takes place(More)
In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their(More)
We present a fast and accurate 3D hand tracking method which relies on RGB-D data. The method follows a model based approach using a hierarchical particle filter variant to track the model's state. The filter estimates the probability density function of the state's posterior. As such, it has increased robustness to observation noise and compares favourably(More)
In this paper we present our research results towards the detection of violent scenes in movies, employing advanced fusion methodologies, based on learning, knowledge representation and reasoning. Towards this goal, a multi-step approach is followed: initially, automated audio and visual analysis is performed to extract audio and visual cues. Then, two(More)