In this paper we propose a novel approach for syntactic description and maching of object trajectories in digital video, suitable for classification and recognition purposes. Trajectories are first segmented by detecting the meaningful discontinuities in time and space, and are successively expressed through an adhoc syntax. A suitable metric is then proposed, which allows determining the similarity among trajectories, based on the so-called inexact or approximate matching. The metric mimics the algorithms used in bioinformatics to match DNA sequences, and returns a score, which allows identifying the analogies among different trajectories on both global and local basis. The tool can therefore be adopted for the analysis, classification, and learning of motion patterns, in activity detection or behavioral understanding.
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In the present work we propose a new approach to dynamically characterize trajectories for a syntactic spatiotemporal alignment that can be applied in the context of behavioral analysis and anomalous activity detection. The developed architecture is based on a symbolic representation of the trajectory, exploiting the framework of the so-called editdistance. The acquired trajectory samples are filtered to identify the most significant spatio-temporal discontinuities: these key points are converted into a string-based domain where the matching of trajectory pairs can be expressed in terms of global alignment between symbols, similarly to DNA string matching algorithms. The extraction, characterization and alignment of trajectories have been tested in different environments, demonstrating the reliability of the achieved results and the viability of the solution for video surveillance and domotics applications.
In this work we present a framework for physical rehabilitation, which is based on hand tracking. One particular requirement in physical rehabilitation is the capability of the patient to correctly reproduce a specific path, following an example provided by the medical staff. Currently, these assignments are typically performed manually, and a nurse or doctor, who supervises the correctness of the movement, constantly assists the patient throughout the whole rehabilitation process. With the proposed system, our aim is to provide medical institutions and patients with a low-cost and portable instrument to automatically assess the rehabilitation improvements. To evaluate the performance of the exercise, and to determine the distance between the trial and the reference path, we adopted the Dynamic Time Warping (DTW) and the Longest Common Sub-Sequence (LCSS) as discriminating metrics. Trajectories and numerical values are then stored to track the history of the patient and appraise the improvements of the rehabilitation process over time. Thanks to the tests conducted with real patients, it has been possible to evaluate the quality of the proposed tool, in terms of both graphical interface and functionalities.
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