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Comprises 4 different subsets - Flat, House, Priory and Lab - each containing a number of different sequences that can be successfully relocalised against each other. Source: CSD

The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

The UCSD Anomaly Detection Dataset was acquired with a stationary camera mounted at an elevation, overlooking pedestrian walkways. The crowd density in the walkways was variable, ranging from sparse to very crowded. In the normal setting, the video contains only pedestrians. Abnormal events are due to either: the circulation of non pedestrian entities in the walkways anomalous pedestrian motion patterns Commonly occurring anomalies include bikers, skaters, small carts, and people walking across a walkway or in the grass that surrounds it. A few instances of people in wheelchair were also recorded. All abnormalities are naturally occurring, i.e. they were not staged for the purposes of assembling the dataset. The data was split into 2 subsets, each corresponding to a different scene. The video footage recorded from each scene was split into various clips of around 200 frames. Source: The UCSD Anomaly Detection Dataset Image Source: http://www.svcl.ucsd.edu/publications/conference/2010/cvpr2010/cvpr_anomaly_2010.pdf

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The dataset consists of images of 158 filled out bank checks containing various complex backgrounds, and handwritten text and signatures in the respective fields, along with both pixel-level and patch-level segmentation masks for the signatures on the checks. Please visit the dataset homepage for more details. If you use this dataset in your work, please include the following citation: Khan, Muhammad Saif Ullah. “A Novel Segmentation Dataset for Signatures on Bank Checks.” ArXiv:2104.12203 [Cs], Apr. 2021. arXiv.org, http://arxiv.org/abs/2104.12203. ##### Acknowledgements [1] P. Dansena, S. Bag, and R. Pal, “Differentiating Pen Inks in Hand-written Bank Cheques Using Multi-Layer Perceptron”, Proc. of 7th International Conference on Pattern recognition and Machine Intelligence, Kolkata, India, December 2017.

DeCost, Hecht, Francis, Webler, Picard, and Holm. UHCSDB (Ultrahigh Carbon Steel micrograph DataBase): tools for exploring large heterogeneous microstructure datasets. accepted for publication in IMMI 2017 doi: 10.1007/s40192-017-0097-0 ABSTRACT: We present a new microstructure dataset consisting of ultrahigh carbon steel (UHCS) micrographs taken over a range of length scales under systematically varied heat treatments. Using the UHCS dataset as a case study, we develop a set of visualization tools for interacting with and exploring large microstructure and metadata datasets. Based on generic microstructure representations adapted from the field of computer vision, these tools enable image-based microstructure retrieval, as well as spatial maps of both microstructure and related metadata, such as processing conditions or properties measurements. We provide the microstructure image data, processing metadata, and source code for these microstructure exploration tools. The UHCS dataset is intended as a community resource for development and evaluation of microstructure data science techniques and for creation of microstructure data science teaching modules.

This MCCS dataset is the first large-scale Mandarin Chinese Cued Speech dataset. This dataset covers 23 major categories of scenarios (e.g, communication, transportation and shoping) and 72 subcategories of scenarios (e.g, meeting, dating and introduction). It is recorded by four skilled native Mandarn Chinese Cued Speech cuers with portable cameras on the mobile phones. The Cued Speech videos are recorded with 30fps and 1280x720 format. We provide the raw Cued Speech videos, text file (with 1000 sentences) and corresponding annotations which contains two kind of data annotation. One is continuious video annotation with ELAN, the other is discrete audio annotations with Praat.

Description: <a href="https://gts.ai/dataset-download/ucsd-pedestrian-database/" target="_blank">👉 Download the dataset here</a> The UCSD Pedestrian Database is a benchmark dataset primarily used for computer vision research, particularly in pedestrian detection, crowd analysis, and video segmentation tasks. Originally featured in the paper “Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures,” this dataset plays a crucial role in advancing algorithms related to video-based object detection and scene understanding. Dataset Description The UCSD Pedestrian Dataset consists of grayscale video footage captured along walkways at the University of California, San Diego (UCSD). The videos are taken from a stationary camera placed at various viewpoints around the campus, ensuring diverse perspectives for researchers to work with. Each video clip contains pedestrian movements recorded under natural conditions, providing an ideal environment for testing pedestrian detection and behavior modeling techniques. Download Dataset Data Format and Structure The videos are record at a resolution of 238×158 pixels, with a frame rate of 10 frames per second (fps), offering a moderate-quality view of pedestrian movement while keeping file sizes manageable. All videos are in 8-bit grayscale format, minimizing color distractions and focusing on movement patterns. The dataset is organize into directories based on different scene locations. Each directory (e.g., vidX, where X is a specific scene identifier such as “vidf”) contains multiple short video clips of pedestrians. Each video clip consists of 200 frames, store as individual .png image files for easier frame-by-frame analysis. The naming convention for each clip follows the format vidfXY ZZZ.y, where Y and ZZZ are numerical identifiers for the video and frame numbers, respectively. Key Features Grayscale Videos: Simplifies the task of detecting moving objects by eliminating color information, making it more suitable for testing motion-detection algorithms. Stationary Camera: Ensures that only the pedestrians are moving, enabling a focus on human behavior without other environmental distractions. Multiple Viewpoints: Although the initial release features only one scene, future expansions of the dataset promise diverse perspectives to enhance the dataset’s robustness. Clip-Base Organization: The videos are broken into 200-frame clips, allowing for manageable chunks of data that can be processed or analyze individually. Use in Dynamic Texture Modeling: This dataset has been historically important for studies in dynamic texture modeling, where pedestrian movement is treat as a time-varying texture for analysis. Applications The UCSD Pedestrian Database is widely use in: Pedestrian Detection: Developing algorithms that accurately identify pedestrians in video sequences. Crowd Behavior Analysis: Studying the collective movement and interactions of pedestrians in public spaces. Scene Segmentation: Separating pedestrians from the background to improve real-time video processing applications. Video Compression: Testing video encoding methods that can maintain quality while reducing file size. Surveillance Systems: Enhancing the accuracy of automate video surveillance for identifying unusual or suspicious pedestrian activity. This dataset is sourced from Kaggle.