Ihmisten asennon tunnistus syvyyskameralla

Human pose estimation has many applications from activity analysis to autonomous cars. Modern advances in deep learning research have enabled real time multi-person pose estimation in complex environments. In this thesis, a state of the art deep learning architecture is adapted to work with depth sensors. A dataset is generated using computer graphics instead of annotating thousands of images by hand. Results are promising; trained neural network detects humans in multi-person environments even when occlusion is present. However, there are challenges rising from the difference between the real world and the synthetic data generation, which has to be addressed.Automaattisella ihmisten asennon tunnistuksella on lukuisia sovelluksia aktiviteettianalyysistä itsenäisiin autoihin. Nykyaikainen kehitys syvien neuroverkkojen alalla on mahdollistanut useamman ihmisen reaaliaikaisen asennon tunnistamisen monimutkaisissa ympäristöissä. Tässä diplomityössä adaptoidaan nykyaikainen neuroverkko arkkitehtuuri toimimaan syvyyskameralla saaduilla kuvilla. Neuroverkon opetukseen tarvittava opetusdata generoidoon tietokonegrafiikan avulla sen sijaan, että opetusdata luotaisiin käsityönä. Saadut tulokset ovat lupaavia; opetettu neuroverkko kykenee tunnistamaan samanaikaisesti usean ihmisen monimutkaisessa ympäristössä. Kaikesta huolimatta, simuloidun datan ja todellisen maailman välinen eroavaisuus aiheuttaa ongelmia, jotka täytyy ottaa huomioon

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms