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Computer Vision

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the 

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The dataset was built by capturing the static gestures of the American Sign Language (ASL) alphabet, from 8 people, except for the letters J and Z, since they are dynamic gestures. To capture the images, we used a Logitech Brio webcam, with a resolution of 1920 × 1080 pixels, in a university laboratory with artificial lighting. By extracting only the hand region, we defined an area of 400 × 400 pixels for the final image of our dataset.
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This dataset contains sheets of handwritten Telugu characters separated in boxes. It contains vowel, consonant, vowel-consonant and consonant-consonant pairs of Telugu characters. The purpose of this dataset is to act as a benchmark for Telugu handwritting related tasks like character recognition. There are 11 sheet layouts that produce 937 unique Telugu characters. Eighty three writers participated in generating the dataset and contributed 913 sheets in all. Each sheet layout contains 90 characters except the last which contains 83 characters where the last 10 are english numerals 0-9.

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A paradigm dataset is constantly required for any characterization framework. As far as we could possibly know, no paradigmdataset exists for manually written characters of Telugu Aksharaalu content in open space until now. Telugu content (Telugu: తెలుగు లిపి, romanized: Telugu lipi), an abugida from the Brahmic group of contents, is utilized to compose the Telugu language, a Dravidian language spoken in the India of Andhra Pradesh and Telangana just a few other neighboring states. The Telugu content is generally utilized for composing Sanskrit writings.

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A benchmark dataset is always required for any classification or recognition system. To the best of our knowledge, no benchmark dataset exists for handwritten character recognition of Manipuri Meetei-Mayek script in public domain so far. Manipuri, also referred to as Meeteilon or sometimes Meiteilon, is a Sino-Tibetan language and also one of the Eight Scheduled languages of Indian Constitution. It is the official language and lingua franca of the southeastern Himalayan state of Manipur, in northeastern India.

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Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.

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Simulated Disaster Victim dataset consists of images and video frames containing simulated human victims in cluttered scenes along with pixel-level annotated skin maps. The simulation was carried out in a controlled environment with due consideration towards the health of all the volunteers. To generate a real effect of a disaster, Fuller’s earth is used which is skin-friendly and does not cause harm to humans. It created an effect of disaster dust over the victims in different situations. The victims included one female and four male volunteers.

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