Discretized categorization of high level traffic activities in tunnels using attribute grammars

Download
2012
Büyüközcü, Demirhan
This work focuses on a cognitive science inspired solution to an event detection problem in a video domain. The thesis raises the question whether video sequences that are taken in highway tunnels can be used to create meaningful data in terms of symbolic representation, and whether these symbolic representations can be used as sequences to be parsed by attribute grammars into abnormal and normal events. The main motivation of the research was to develop a novel algorithm that parses sequences of primitive events created by the image processing algorithms. The domain of the research is video detection and the special application purpose is for highway tunnels, which are critical places for abnormality detection. The method used is attribute grammars to parse the sequences. The symbolic sequences are created from a cascade of image processing algorithms such as; background subtracting, shadow reduction and object tracking. The system parses the sequences and creates alarms if a car stops, moves backwards, changes lanes, or if a person walks into the road or is in the vicinity when a car is moving along the road. These critical situations are detected using Earley’s parser, and the system achieves real-time performance while processing the video input. This approach substantially lowers the number of false alarms created by the lower level image processing algorithms by preserving the number of detected events at a maximum. The system also achieves a high compression rate from primitive events while keeping the lost information at minimum. The output of the algorithm is measured against SVM and observed to be performing better in terms of detection and false alarm performance.

Suggestions

GESwarm Grammatical Evolution for the Automatic Synthesis of Collective Behaviors in Swarm Robotics
Ferrante, Eliseo; Turgut, Ali Emre; DuenezGuzman, Edgar; Wenseleers, Tom (2013-07-10)
In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main caveat of neural networks is that they are very difficult to reverse engineer, meaning that once a suitable solution is found, it is very difficult to analyze, to ...
Birinci-Şahıs Videolarda Aktivite Tanıma İçin Sıralamalı Takviyeli Çoklu Çekirdek Öğrenmesi
Özkan, Fatih; Sürer, Elif; Temizel, Alptekin (2018-05-05)
In this paper, we investigate fusion of different types of classifiers for activity recognition on first-person videos in a data-driven approach. The algorithm first uses the classifiers, which are composed of kernel and descriptor combinations, through well-known AdaBoost trials. After all trials, classifiers are ordered and assigned ranks with respect to their performances in each trial separately. These classifiers compose a candidate list according to their performance ranks. Classifiers in the candidat...
FUNCTIONAL NETWORKS OF ANATOMIC BRAIN REGIONS
Velioglu, Burak; Aksan, Emre; Onal, Itir; Firat, Orhan; Ozay, Mete; Yarman Vural, Fatoş Tunay (2014-08-20)
In this study, we propose a new approach to construct a two-level functional brain network. The nodes of the first-level network are the voxels of the functional Magnetic Resonance Images (tMRI) recorded during an object recognition task. The nodes of the network at the second-level are the anatomic regions of the brain. The arcs of the first level are estimated by a linear regression equation for the meshes formed around each voxel. Neighbors of each voxel are determined by using a functional similarity me...
Supervised and unsupervised models of brain networks for brain decoding
Alchihabi, Abdullah; Yarman Vural, Fatoş Tunay; Önal Ertuğrul, Itır; Department of Computer Engineering (2018)
In this thesis, we propose computational network models for human brain. The models are estimated from fMRI measurements, recorded while subjects perform a set of cognitive tasks. We employ supervised and unsupervised machine learning techniques to represent high level cognitive tasks of human brain by dynamic networks. In the first part of this thesis, we propose an unsupervised multi-resolution brain network model. First, we decompose the signal into multiple sub-bands using Wavelet transform and estimate...
Hydrodynamic picture of light trapping in integrated photonic nanostructures and metamaterials
Boriskina, Svetlana V.; Yerci, Selçuk; Chen, Gang (null; 2013-01-01)
We introduce a concept of light trapping on the nanoscale via pinning optical vortices to optimally-designed nanostructures, reveal this mechanism behind known focusing phenomena, and offer new approaches to design nanophotonic platforms and/or coupling schemes. © 2013 Optical Society of America.
Citation Formats
D. Büyüközcü, “Discretized categorization of high level traffic activities in tunnels using attribute grammars,” M.S. - Master of Science, Middle East Technical University, 2012.