Probabilistisch-logische Inferenz relationaler Situationsbeschreibungen aus Verkehrsbildfolgen

Fahrerassistenzsysteme mit maschineller Umfeldwahrnehmung gewinnen an Bedeutung. Die vorliegende Arbeit präsentiert ein auf Markov-Logik-Netzen basierendes Modell, mit welchem eine Situationsbeschreibung einer Verkehrsszene probabilistisch-logisch inferiert wird. Dieser Formalismus ermöglicht eine relationale Repräsentation von komplexen Diskursbereichen, in welcher probabilistisches Schließen durchgeführt wird. Der Ansatz wird anhand exemplarisch verwendeter Videosequenzen evaluiert

Information density and visual activity in dynamic pictures

Zbog velikog utjecaja na društvo vizualni mediji tema su kontinuiranog istraživanja u ovom i u prošlom stoljeću. U ovoj disertaciji predložen je i provjeren model mjerenja vizualne aktivnosti medija koji se temelji na algoritmima za uklanjanje pozadine poznatim u području računalnog vida. Algoritmi za uklanjanje pozadine obrađuju video sadržaje na način da se kao izlaz obrade dobije dvobojni video sadržaj, pri čemu je jedna boja detektirani pokretni objekt, dok je druga nepomična pozadina. Koristeći omjer broja točaka detektiranih pokretnih objekata i ukupnog broja točaka u nekom video sadržaju može se kvantificirati količina pokreta, što je ključna ideja ove disertacije. S obzirom da je dostupno nekoliko desetaka različitih algoritama za uklanjanje pozadine, za izbor odgovarajućeg algoritma korišten je Model ograničenog kapaciteta obrade motiviranih posredovanih poruka, kojim je moguće kvantificirati gustoću informacija video sadržaja. Nakon izbora algoritma za uklanjanje pozadine, predloženi model uspoređen je s postojećim modelom za mjerenje vizualne aktivnosti, tj. Cuttingovim Indeksom vizualne aktivnosti pri čemu je dobiven vrlo visok koeficijent korelacije rs = 0,823. Potom je testirana robusnost obaju modela s obzirom na šum. Provjera predloženog modela provedena je na skupu od 50 filmova koji su dobili nagradu Academy Award for Best Picture između 1965. i 2014. godine, te 30 video spotova koji su dobili nagradu MTV Best Music Video između 1984. i 2013. godine. Uočen je statistički značajan rast vrijednosti, odnosno predložena mjera ukazuje na porast vizulane aktivnosti kroz vrijeme.Due to their large influence on the society visual media have been a subject of research in the last two centuries. New opportunities for analysis of visual media have been created through digitisation and development of information and communication technologies. A phenomenon observed by many authors is constant growth of video content activity. This thesis puts forward the method of visual media activity measurement based on the background subtraction algorithms that are known for computer vision systems. Gibson (1954) defines the term visual activity as the totality of movement of objects and people along a constant background and visual information gained through movement of the observer. The problem of visual activity measurment stems from the fact that it is desirable to include the recipient of the information because the amount of emitted visual information and perceived visual activity are not necessarily in a correlation. The background subtraction algorithms process video content in such manner that output of the processing is a two-colour video content where one colour is a detected movable object, while the other is stationary background. The key concept in this thesis is that it is possible to quantify movement by using a ratio of the number of pixels of the detected movable objects and the total number of pixels in the given video content. Since several dozen different background subtraction algorithms are available, the Limited Capacity Model of Motivated Mediated Message Processing was used in order to select the suitable one. It is a model capable of quantification of video information density. The model itself also includes recipients of visual information. Following the selection of the background subtraction algorithms, the proposed model for visual activity measuring was compared with the existing visual activity measurement model – Cutting’s visual activity index – and a strong correlation of rs = 0.823 was observed. It is obvious that both models take into consideration similar properties of video content. Furthermore, due to properties of the underlying algorithms themselves, the proposed visual activity measurement model based on background subtraction algorithms is significantly less susceptible to noise than Cutting’s visual activity index. That has also been proven by introduction of noise in video content which led to significant increases of the visual activity index – even in cases of comparably low noise levels – while the same was not observed with the background subtraction visual activity index. It is to be expected that the visual activity mesuring model based on background subtraction will be less sensitive to application of varying levels of video content compression than Cutting’s visual activity index. The final testing of the proposed model was performed using two groups of video content. The first group consisted of 50 recipients of the Academy Award for Best Picture presented by the Academy of Motion Picture Arts and Sciences between 1965 and 2014. The second group consisted of recipients of the MTV Best Music Video award presented between 1984 and 2013. The Mann-Kendall Trend Test was used to test background subtraction visual activity index changes concerning the award-winning feature films in the above 5-decade period. It was revealed that the value grew in the period. The same test was applied to test the changes in the award-winning music videos in the above 3-decade period – likewise revealing a growth of the background subtraction visual activity index. As an alternative to the Cutting’s visual activity index, the background subtraction visual activity index shall allow scientists in the field of humanities and social sciences to quantify visual activity which is hitherto described in qualitative terms in their works. A potential opportunity for further development and automation of the Limited Capacity Model of Motivated Mediated Message Processing is also introduced because measurement of certain aspects of information density could be automated

Using Behavioral Knowledge for Situated Prediction of Movements

The textual description of video sequences exploits conceptual knowledge about the behavior of depicted agents. An explicit representation of such behavioral knowledge facilitates not only the textual description of video evaluation results, but can also be used for the inverse task of generating synthetic image sequences from textual descriptions of dynamic scenes. Moreover, it is shown here that the behavioral knowledge representation within a cognitive vision system can be exploited even for prediction of movements of visible agents, thereby improving the overall performance of a cognitive vision system