6 research outputs found
A Nonlinear-Shift Approach to Object Tracking Based on Shape Information
No full textThis paper presents a corner-based method for tracking objects in video image frames. The method uses a vectorial shape representation based on relative object main corners positions and a non-linear voting method to evaluating the new object position at each iteration. The initialization consists of individuating an area including the object to be tracked. Information of the corners distribution around a reference point is used to find the most probable target position in the next frame. The method can be used in both fixed and mobile cameras both for vehicles and pedestrians
Tracking by using dynamic shape model learning in the presence of occlusion
No full textThe paper presents a new corner-model based learning method able to track non-rigid objects in the presence of occlusion. A voting mechanism followed by a probability density analysis of the voting space histogram is used to estimate new position of the target. The model is updated at any frame. The problem rises in the occlusion events where the occluder corners affect the model and the tracker may follow the occluder. The key point of the method toward success is automatically deciding on the corners to classify them into two classes, good and malicious corners. Good corners are used to update the model in a conservative way removing the corners that are voting to the highly voted wrong positions due to the occluder. This leads to a continuous model learning during occlusion. Experimental results show a successful tracking along with a more precise estimation of shape and motion durin
A Bayesian Framework for Online Interaction Classification
No full textReal-time automatic human behavior recognition is one
of the most challenging tasks for intelligent surveillance
systems. Its importance lies in the possibility of robust detection
of suspicious behaviors in order to prevent possible
threats. The widespread integration of tracking algorithms
into modern surveillance systems makes it possible to acquire
descriptive motion patterns of different human activities.
In this work, a statistical framework for human interaction
recognition based on Dynamic Bayesian Networks
(DBNs) is presented: the environment is partitioned by a
topological algorithm into a set of zones that are used to define
the state of the DBNs. Interactive and non-interactive
behaviors are described in terms of sequences of significant
motion events in the topological map of the environment.
Finally, by means of an incremental classification measure,
a scenario can be classified while it is currently evolving.
In this way an autonomous surveillance system can detect
and cope with potential threats in real-tim
"Multi-camera indoor video processing for context awareness"
No full textIn this paper a system is presented able to acquire images from multiple indoor network cameras and extract contextual information about persons detected within the considered environment. Distributed system architecture allows one to process images from several cameras on a network of PCs. Objects tracking and posture classification techniques are used in order to extract contextual information from video. These information are stored in a remote database that is accessed from an higher level application that is able to interact with users' mobile phones for delivering context-based-services. In particular, proposed scene understanding techniques have been used for implementing an automatic terminal silencing service in case of a meeting and a sos-call in case of a falling person
