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Sharma, 'Background-Subtraction using Contour-based Fusion of Thermal and Visible Imagery,' Computer Vision and Image Understanding, Vol 106, No. Number of color/thermal sequences = 6 (3 at each location)ДЇormat of images = Thermal: 8-bit grayscale bitmap, Color: 24-bit color bitmapД¬olor/Thermal images registered using homography with manually-selected pointsДЇiles containing tracking results on the dataset are provided by Alex Leykin Thermal Sensor: Raytheon PalmIR 250D, 25 mm lensД¬ameras mounted adjacent to each other on tripod at two locations approximately 3 stories above groundД«usy pathway intersections on the Ohio State University campus Office of Naval Research under grant #N000143010022.ДЇusion-based object detection in color and thermal imagery Illumination: Lon (left light on), Ron (right light on), 2on (both lights on), dark (dark room), off (left and right lights off), varying posesДOE University Research Program in Robotics under grant DOE-DE-FG02-86NE37968 ДOD/TACOM/NAC/ARC Program under grant R01-1344-18 Image size: 320 x 240 pixels (visible and thermal)ДЎ76-250 images/person, 11 images per rotation (poses for each expression and each illumination)ДЈ0 individuals - Expression, pose, and illuminationД®xpression: ex1, ex2, ex3 - surprised, laughing, angry (varying poses) Under variable illuminations, expressions, and poses. Simultaneously acquired unregistered thermal and visible face images Davis, Дataset 02: IRIS Thermal/Visible Face Database Workshop on Applications of Computer Vision, January 2005

'A two-stage approach to person detection in thermal imagery,' Least 50% visible in the image (i.e., highly occluded people were not selected). (with approximately same aspect ratio) around people.ДЇor the ground truth data, we selected only those people that were at Ground truth provided in subdirectories as list of bounding boxes Sampling rate = non-uniform, less than 30HzД®nvironmental information for each sequence provided in subdirectories Pedestrian intersection on the Ohio State University campusДЇormat of images = 8-bit grayscale bitmap Should be sent to the benchmark steering committee atДataset 01: OSU Thermal Pedestrian DatabaseД¬amera mounted on rooftop of 8-story building Questions or comments regarding this benchmark This benchmark must be acknowledged by the users. This benchmark is to be used for educational and research purposes only, and Guoliang Fan at Oklahoma State University. Participants to explore the benefits of the non-visible spectrum in real-world applications,Д¬ontribute to the OTCBVS workshop series, and boost this research field significantly. To all researchers in the international computer vision communities.ДЄlso it will allow a large spectrum of IEEE and SPIE vision conference and workshop

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The benchmark contains videos and images recorded in and beyond the visible spectrum and is available for free Several researchers and students have requested a benchmark of non-visible (e.g., infrared) images and videos. State-of-the-art computer vision algorithms. This is a publicly available benchmark dataset for testing and evaluating novel and

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OTCBVS OTCBVS Benchmark Dataset Collection OTCBVSДЄddress of OTCBVS Benchmark has changed and please update your bookmarks.

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