Computer Vision

This is the collection of Indian Traffic Sign Detection Dataset. This can be used maily on Traffic Sign detection projects using YOLO. Dataset is in YOLO format. There are 1264 total images in this dataset fully annotated using Labelimg tool. Some augmented datas using techniques like blurring, mosaic etc.. are also present. The dataset has images in 3 different types of traffic signs in India. Dataset is annotated only as one class-Traffic Sign.

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This is the collection of Indian Traffic Sign Detection Dataset. This can be used maily on Traffic Sign detection projects using YOLO. Dataset is in YOLO format. There are 1264 total images in this dataset fully annotated using Labelimg tool. Some augmented datas using techniques like blurring, mosaic etc.. are also present. The dataset has images in 3 different types of traffic signs in India. Dataset is annotated only as one class-Traffic Sign.

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2099 Views

Today, the cameras are fixed everywhere, in streets, in vehicles, and in any public area. However, Analysis and extraction of information from images are required. Particularly, in autonomous vehicles and in smart applications that are developed to guide tourists. So, a large dataset of scene text images is an important and difficult factor in the extraction of textual information in natural images. It is the input to any computer vision system.

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This dataset contains a collection of videos consisting of satellite imagery augmented with 3D ship models, accompanied by the ships' corresponding AIS data. The intention of this dataset is for detecting dark ships, which are sea vessels acting maliciously, often while spoofing their AIS data. Multiple datasets exist that consist of satellite imagery of ships, however this dataset has the advantage of including each ships' corresponding AIS data. The simulated ships include both normal and anomalous behavior, whether the anomalous behavior is benign or malicious.

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The Contest: Goals and Organization

The 2022 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee, aims to promote research on semi-supervised learning. The overall objective is to build models that are able to leverage a large amount of unlabelled data while only requiring a small number of annotated training samples. The 2022 Data Fusion Contest will consist of two challenge tracks:

Track SLM:Semi-supervised Land Cover Mapping

Last Updated On: 
Mon, 03/07/2022 - 04:41

To ensure the usability and reliability of the collected data, one Hikvision monitoring camera (iDS-TCV900-AE/25) is deployed at the entrance of Taijia Expressway in Shanxi province in China for image capturing. This camera is installed on the roadside pole with a height of 5.8 meters and uses the infrared flash as the supplementary lighting. The captured images cover two lanes of the expressway, with the resolution being 4096*2160. All images are captured during the period of November 2019 to April 2020.

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Nankai Chinese Font Style dataset contains both handwriting and standard printing. Furthermore, the Chinese characters in each font style include not only the fifirst-level simplifified Chinese characters,but also some rare characters and ancient Chinese charactersthat cannot be represented by Unicode encoding. 

 

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We present below a sample dataset collected using our framework for synthetic data collection that is efficient in terms of time taken to collect and annotate data, and which makes use of free and open source software tools and 3D assets. Our approach provides a large number of systematic variations in synthetic image generation parameters. The approach is highly effective, resulting in a deep learning model with a top-1 accuracy of 72% on the ObjectNet data, which is a new state-of-the-art result.

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This is the supplemental material to the paper "fast computation of neck-like features".

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The MPSC-rPPG dataset comprises photoplethysmograph (rPPG) data with the PPG ground truth, making it a perfect dataset to evaluate various algorithms for extracting PPG, measuring heart rate, heart rate variability from video. The dataset contains facial videos and Blood Volume Pulse (BVP) data captured concurrently.

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