  Action recognition has received increasing attention from the computer vision
and machine learning communities in the last decade. To enable the study of
this problem, there exist a vast number of action datasets, which are recorded
under controlled laboratory settings, real-world surveillance environments, or
crawled from the Internet. Apart from the "in-the-wild" datasets, the training
and test split of conventional datasets often possess similar environments
conditions, which leads to close to perfect performance on constrained
datasets. In this paper, we introduce a new dataset, namely Multi-Camera Action
Dataset (MCAD), which is designed to evaluate the open view classification
problem under the surveillance environment. In total, MCAD contains 14,298
action samples from 18 action categories, which are performed by 20 subjects
and independently recorded with 5 cameras. Inspired by the well received
evaluation approach on the LFW dataset, we designed a standard evaluation
protocol and benchmarked MCAD under several scenarios. The benchmark shows that
while an average of 85% accuracy is achieved under the closed-view scenario,
the performance suffers from a significant drop under the cross-view scenario.
In the worst case scenario, the performance of 10-fold cross validation drops
from 87.0% to 47.4%.
