Event Detection in Videos: A Framework for the Development of New Methods
For researchers in video event detection, this framework aims to standardize method development and comparison, but it is a conceptual proposal without empirical validation.
The paper addresses the lack of standardized evaluation in video event detection by proposing a framework based on datasets, performance evaluation, and deployment scenarios. No concrete results are provided.
Event detection tasks in videos, the most important aspect of video surveillance, aim to detect events either at the pixel-level, frame-level, or clip-level. Plenty of methods intended for event detection in different environments, for various applications, and within different acquisition techniques were introduced. Naturally, the attempts were made as well to classify these algorithms in terms of detection of performance or in terms of real-time abilities. Nevertheless, the lack of a large-scale dataset as well as rigorous performance evaluation methods have biased such comparisons as well as the development of the methods. Given the diversity of existing approaches, we believe it is essential for researchers to position their work within such a rich landscape. Thus, we propose a rigorous framework for developing new methods in event detection for videos. Specifically, this framework is based on three main pillars: datasets, performance evaluation, and scenarios for deploying methods.