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    Title :

    Implementation of an Approach for 3D Vehicle Detection in Monocular Traffic Surveillance Videos


    Contributors:

    Publication date :

    2021


    Size :

    1 Online-Ressource (ca. 123 Seiten)


    Remarks:

    Recent advancements in the field of Computer Vision are a by-product of breakthroughs in the domain of Artificial Intelligence. Object detection in monocular images is now realized by an amalgamation of Computer Vision and Deep Learning. While most approaches detect objects as a mere two dimensional (2D) bounding box, there are a few that exploit rather traditional representation of the 3D object. Such approaches detect an object either as a 3D bounding box or exploit its shape primitives using active shape models which results in a wireframe-like detection. Such a wireframe detection is represented as combinations of detected keypoints (or landmarks) of the desired object. Apart from a faithful retrieval of the object’s true shape, wireframe based approaches are relatively robust in handling occlusions. The central task of this thesis was to find such an approach and to implement it with the goal of its performance evaluation. The object of interest is the vehicle class (cars, mini vans, trucks etc.) and the evaluation data is monocular traffic surveillance videos collected by the supervising chair. A wireframe type detection can aid several facets of traffic analysis by improved (compared to 2D bounding box) estimation of the detected object’s ground plane. The thesis encompasses the process of implementation of the chosen approach called Occlusion-Net [40], including its design details and a qualitative evaluation on traffic surveillance videos. The implementation reproduces most of the published results across several occlusion categories except the truncated car category. Occlusion-Net’s erratic detections are mostly caused by incorrect detection of the initial region of interest. It employs three instances of Graph Neural Networks for occlusion reasoning and localization. The thesis also provides a didactic introduction to the field of Machine and Deep Learning including intuitions of mathematical concepts required to understand the two disciplines and the implemented approach.


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    006 / 006.37 / 620 / 380
    RVK:    ZO 4620





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