Browsing by Subject "object detection"

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  • Laitala, Julius (Helsingin yliopisto, 2021)
    Arranging products in stores according to planograms, optimized product arrangement maps, is important for keeping up with the highly competitive modern retail market. The planograms are realized into product arrangements by humans, a process which is prone to mistakes. Therefore, for optimal merchandising performance, the planogram compliance of the arrangements needs to be evaluated from time to time. We investigate utilizing a computer vision problem setting – retail product detection – to automate planogram compliance evaluation. We introduce the relevant problems, the state-of- the-art approaches for solving them and background information necessary for understanding them. We then propose a computer vision based planogram compliance evaluation pipeline based on the current state of the art. We build our proposed models and algorithms using PyTorch, and run tests against public datasets and an internal dataset collected from a large Nordic retailer. We find that while the retail product detection performance of our proposed approach is quite good, the planogram compliance evaluation performance of our whole pipeline leaves a lot of room for improvement. Still, our approach seems promising, and we propose multiple ways for improving the performance enough to enable possible real world utility. The code used for our experiments and the weights for our models are available at https://github.com/laitalaj/cvpce
  • Jaakkola, A (Aalto University, 2015)
    Aalto University publication series DOCTORAL DISSERTATIONS
  • Chumachenko, Kateryna; Männistö, Anssi; Iosifidis, Alexandros; Raitoharju, Jenni (IEEE, 2020)
    IEEE Access 8 (2020)
    In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives. Specifically, we analyze prominent Finnish World War II photographers, who have captured high numbers of photographs in the publicly available Finnish Wartime Photograph Archive, which contains 160,000 photographs from Finnish Winter, Continuation, and Lapland Wars captures in 1939-1945. We were able to find some special characteristics for different photographers in terms of their typical photo content and framing (e.g., close-ups vs. overall shots, number of people). Furthermore, we managed to train a neural network that can successfully recognize the photographer from some of the photos, which shows that such photos are indeed characteristic for certain photographers. We further analyzed the similarities and differences between the photographers using the features extracted from the photographer classifier network. We make our annotations and analysis pipeline publicly available, in an effort to introduce this new research problem to the machine learning and computer vision communities and facilitate future research in historical and societal studies over the photo archives.
  • Rönnholm, Petri; Vaaja, Matti; Kauhanen, Heikki; Klockars, Tuomas (2020)
    In this paper, we illustrate how convolutional neural networks and voxel-based processing together with voxel visualizations can be utilized for the selection of unaimed images for a photogrammetric image block. Our research included the detection of an ear from images with a convolutional neural network, computation of image orientations with a structure-from-motion algorithm, visualization of camera locations in a voxel representation to detect the goodness of the imaging geometry, rejection of unnecessary images with an XYZ buffer, the creation of 3D models in two different example cases, and the comparison of resulting 3D models. Two test data sets were taken of an ear with the video recorder of a mobile phone. In the first test case, a special emphasis was taken to ensure good imaging geometry. On the contrary, in the second test case the trajectory was limited to approximately horizontal movement, leading to poor imaging geometry. A convolutional neural network together with an XYZ buffer managed to select a useful set of images for the photogrammetric 3D measuring phase. The voxel representation well illustrated the imaging geometry and has potential for early detection where data is suitable for photogrammetric modelling. The comparison of 3D models revealed that the model from poor imaging geometry was noisy and flattened. The results emphasize the importance of good imaging geometry.