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 & Electronics
II - Intelligent Imaging
TS - Technical Sciences
Safety and Security
Image processing
Defence, Safety and Security
Threat recognition
Complex actions
Temporal relations
Multi-threaded parsing
Stochastic parsing
Video 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