ANNO: A Time Series Annotation Tool to Evaluate Event Detection Algorithms

Huchtkoetter, Jana; Reinhardt, Andreas; Hossain, Sakif

doi:10.1007/978-3-030-45718-1_5

Cited by 1 publication

(1 citation statement)

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“…Among the most common annotations, available in almost all tools, for example is the ability to easily draw a rectangle or an ellipse. Some tools allow one to annotate a polygon when it is convenient (e.g., 3D objects) [16], keypoints (e.g., human pose [17]), temporal events [18], or the association of objects between frames of the video [12,17]. Semi-automatic approaches exist to optimize the annotation process by reducing human interactions e.g, with recognition of human activity [19], detection of objects and segmentation throughout a video [20][21][22], as well as methods based on single frame detection such as [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Cloud-Native Video Annotation for Autonomous Driving

et al. 2020

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An innovative solution named Annotation as a Service (AaaS) has been specifically designed to integrate heterogeneous video annotation workflows into containers and take advantage of a cloud native highly scalable and reliable design based on Kubernetes workloads. Using the AaaS as a foundation, the execution of automatic video annotation workflows is addressed in the broader context of a semi-automatic video annotation business logic for ground truth generation for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). The document presents design decisions, innovative developments, and tests conducted to provide scalability to this cloud-native ecosystem for semi-automatic annotation. The solution has proven to be efficient and resilient on an AD/ADAS scale, specifically in an experiment with 25 TB of input data to annotate, 4000 concurrent annotation jobs, and 32 worker nodes forming a high performance computing cluster with a total of 512 cores, and 2048 GB of RAM. Automatic pre-annotations with the proposed strategy reduce the time of human participation in the annotation up to 80% maximum and 60% on average.

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