Video Object Segmentation

With the widespread adoption of digital technologies, particularly mobile devices, the amount of available video data has skyrocketed. As a result, tools are urgently needed to facilitate automatic video analysis and comprehension. In this context, video object segmentation (VOS) is a crucial task, with potentially benefited areas ranging from video processing activities, like video compression and captioning, to a variety of applications, including visual tracking, video-based question answering, human pose estimation, surveillance and autonomous driving. The main goal of VOS is to classify all the pixels along a frame sequence into foreground and background regions.

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 Thesis  MENDONÇA, MARCELO. Introducing a self-supervised, superfeature-based network for video object segmentation. 129 p.

 Conference  MENDONÇA, M.; FONTINELE, J.; OLIVEIRA, L.; SHLS: Superfeatures learned from still images for self-supervised VOS. In: British Machine Vision Conference (BMVC'2023), 2023.

Video summarization

Hours of video are uploaded to streaming platforms every minute, with recommender systems suggesting popular and relevant videos that can help users save time in the searching process. Recommender systems regularly require video summarization as an expert system to automatically identify suitable video entities and events. Since there is no well-established methodology to evaluate the relevance of summarized videos, some studies have made use of user annotations to gather evidence about the effectiveness of summarization methods.

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 Thesis  ABDALLA, KALYF. From modeling perceptions to evaluating video summarizers. 74 p.

 Journal  ABDALLA, K; MENEZES, I.; OLIVEIRA, L. Modelling perceptions on the evaluation of video summarization. In: Elsevier Expert Systems with Applications, 2019.

Traffic analysis

Correctly identifying the road area on an image is a crucial task for many traffic analyses based on surveillance cameras and computer vision. Despite that, most of the systems do not provide this functionality in an automatic fashion; instead, the road area needs to be annotated by tedious and inefficient manual processes. This situation results in further inconveniences when one deals with a lot of cameras, demanding considerable effort to setup the system. Besides, since traffic analysis is an outdoor activity, cameras are exposed to disturbances due to natural events (e.g., wind, rain and bird strikes), which may require recurrent system reconfiguration.

Check out this background subtraction library at Github

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 Thesis  FONTINELE, JEFFERSON. Paying attention to the boundaries in semantic image segmentation. 74 p.

 Conference  BARROS, J.; OLIVEIRA, L. Deep Speed Estimation from Synthetic and Monocular Data. In: IEEE International Symposium on Intelligent Vehicle, 2021.

 Conference  ASCENSAO, N.; AFONSO, L.; COLOMBO, D.; Oliveira, L.; PAPA, J. P. Information Ranking Using Optimum-Path Forest. In: IEEE World Congress on Computational Intelligence, 2020, Glasgow. International Joint Conference on Neural Network, 2020.

 Conference  FONTINELE, J.; MENDONÇA, M.; RUIZ, M.; PAPA, J.; OLIVEIRA, L. Faster α-expansion via dynamic programming and image partitioning. IEEE World Congress on Computational Intelligence, 2020, Glasgow. International Joint Conference on Neural Network, 2020.

 Journal  SANTOS, M.; OLIVEIRA, L. ISEC: Iterative over-Segmentation via Edge Clustering. In: Elsevier Image and Vision Computing, 2018.

 Conference  SANTOS, M.; OLIVEIRA, L. Context-supported Road Information for Background Modeling. In: XVIII Conference on Graphics, Patterns and Images (SIBGRAPI), Salvador, 2015. 8 p.

 Dissertation  SANTOS, M. Road Detection in Traffic Analysis: A Context-aware Approach. 110 p.

 Conference  SANTOS, M.; LINDER, M.; SCHNITMAN, L.; NUNES, U.; OLIVEIRA, L. Learning to segment roads for traffic analysis in urban images. In: IEEE Intelligent Vehicles Symposium, Gold Coast City, 2013. 6 p.

 Conference  ANDREWS, S.; OLIVEIRA, L. SCHNITMAN, L.; SOUZA, F. (Best Paper) Highway Traffic Congestion Classification Using Holistic Properties. In: 15th International Conference on Signal Processing (ICSP), Pattern Recognition and Applications, Amsterdam, 2013. 8 p.

Synthetic data sets

To generate any data set, three steps are required: locate 3D models in the scene, set the discretization parameters, and finally run the generator. The set of rendered images provide data that can be used for geometric pattern recognition problems, such as depth estimation, camera pose estimation, 3D box estimation, 3D reconstruction, camera calibration, and also a pixel-perfect ground-truth for scene understanding problems (e.g., semantic segmentation, instance segmentation, object detection, just to cite a few).

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 Conference  RUIZ, M.; FONTINELE, J.; PERRONE, R.; SANTOS, M.; OLIVEIRA, L. A Tool for Building Multi-purpose and Multi-pose Synthetic Data Sets. In: ECCOMAS THEMATIC CONFERENCE ON COMPUTATIONAL VISION AND MEDICAL IMAGE PROCESSING, Lecture Notes in Computational Vision and Biomechanics, 2019.

 Dissertation  RUIZ, M. A tool for building multi-purpose and multi-pose, synthetic data set. 103 p.

Camera auto-calibration

Surveillance cameras are commonly used in public and private security systems. This kind of equipment allows automation of surveillance tasks, when integrated with intelligent pattern recognition systems. Camera calibration allows intelligent systems to use the 3D geometry of a scene as a tool to determine the position and size of a target object. Typical systems may contain a large number of cameras, which are installed in different locations, and they are composed of static and heterogeneous cameras.

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 Conference  TROCOLI, T.; OLIVEIRA, L. Using the scene to calibrate the camera. In: XIX Conference on Graphics, Patterns and Images (SIBGRAPI), Sao Jose dos Campos, 2016. 7 p.

 Dissertation  SOUZA, T. T. L. Auto-calibração de camêras de vídeo-vigilância por meio de informações da cena. 104 p.