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Most of the traffic accidents are caused by human related factors. One of the most important human related factor in terms of traffic accident risk is aggressive driving behavior. Although driver aggressiveness is related with psychological reasons, it can be detected by observation of driving behavior using different sensing devices. This work presents a driver aggressiveness detection method exploiting the visual data obtained by on vehicle camera. The proposed method uses this visual data in order to extract features such as lane departure rate and possible collision time by detection of road lines and vehicles on the road. These features are used to train a classifier which decides on whether an aggressive behavior is performed in related driving session. According to conducted tests the proposed method is observed to achieve 90% correct detection rate of aggressive driving behavior.

In this paper, scene recognition problem is investigated in detail by exploiting scene representation, dimension reduction and classification stages. Unlike the other studies, the proposed algorithm has preferred to model the overall structure of the scene instead of an object-based proposal. For that purpose, some of the visual representations like MPEG-7, Gist, BoW, Vlad and Fisher, are classified singly or jointly with Support Vector Machine and Random Forest. The evaluation tests are conducted on MIT indoor dataset [2] and from the results, %31 average precision has been attained by combining Scalable Color, Homogeneous Texture and Vlad with Support Vector Machine.

23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY NUR YILMAZ, Gokce/0000-0002-0015-9519; B. Akar, Gozde/0000-0002-4227-5606 WOS: 000380500900222 In this study, a novel, content aware audiovisual quality assessment (AVQA) method using a spatio-temporal characteristics based video classification method has been proposed and evaluated on AVQA database created by University of Plymouth. The proposed AVQA method is evaluated using subjective audio mean opinion score (MOS) and subjective video MOS. Results indicate that both classification method and the proposed content dependent AVQA method are quite satisfactory Dept Comp Engn & Elect & Elect Engn, Elect & Elect Engn, Bilkent Univ