Understanding human actions in videos has been a central research theme in Computer Vision for decades, and much progress has been achieved over the years. Much of this progress was demonstrated on standard benchmarks used to evaluate novel techniques. These benchmarks and their evolution, provide a unique perspective on the growing capabilities of computerized action recognition systems. They demonstrate just how far machine vision systems have come while also underscore the gap that still remains between existing state-of-the-art performance and the needs of real-world applications. In this paper we provide a comprehensive survey of these benchmarks: from early examples, such as the Weizmann set , to recently presented, contemporary benchmarks. This paper further provides a summary of the results obtained in the last couple of years on the recent ASLAN benchmark , which was designed to reflect the many challenges modern Action Recognition systems are expected to overcome.