Print Email Facebook Twitter A Unified Approach to the Recognition of Complex Actions from Sequences of Zone-Crossings Title A Unified Approach to the Recognition of Complex Actions from Sequences of Zone-Crossings Author Sanromà, G. Patino, L. Burghouts, G.J. Schutte, K. Ferryman, J. Publication year 2014 Abstract We present a method for the recognition of complex actions. Our method combines automatic learning of simple actions and manual definition of complex actions in a single grammar. Contrary to the general trend in complex action recognition, that consists in dividing recognition into two stages, our method performs recognition of simple and complex actions in a unified way. This is performed by encoding simple action HMMs within the stochastic grammar that models complex actions. This unified approach enables a more effective influence of the higher activity layers into the recognition of simple actions which leads to a substantial improvement in the classification of complex actions. We consider the recognition of complex actions based on person transits between areas in the scene. As input, our method receives crossings of tracks along a set of zones which are derived using unsupervised learning of the movement patterns of the objects in the scene. We evaluate our method on a large dataset showing normal, suspicious and threat behavior on a parking lot. Experiments show an improvement of ~ 30% in the recognition of both high-level scenarios and their composing simple actions with respect to a two-stage approach. Experiments with synthetic noise simulating the most common tracking failures show that our method only experiences a limited decrease in performance when moderate amounts of noise are added. Subject Physics & ElectronicsII - Intelligent ImagingTS - Technical SciencesSafety and SecurityImage processingDefence, Safety and SecurityThreat recognitionComplex actionsTemporal relationsMulti-threaded parsingStochastic parsingVideo images To reference this document use: http://resolver.tudelft.nl/uuid:1658cee3-7d12-4267-a87b-68e6463cac73 DOI https://doi.org/10.1016/j.imavis.2014.02.005 TNO identifier 489162 Source Image and Vision Computing, 32, 363-378 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.