Activity Normalization for Activity Detection in Surveillance Videos

Takashi Hosono, Kiyohito Sawada, Yongqing Sun, Kazuya Hayase, Jun Shimamura

Abstract: A framework for activity detection in surveillance videos generally involves activity proposal generation and activity classification. An activity proposal is a spatial and temporal candidate region for an arbitrary activity, and an activity classifier identifies the activity class for activity proposals. One of the difficulties in activity classification is the variation in the number of activity appearances due to the diversity of the object moving directions and inter-object-positional relationships. To solve this problem, we propose an activity normalization method for rotating activity proposals so that the object-movement direction and inter-object-positional relationship are constant among all activity proposals before classification. The experimental results indicate that activity classification accuracy improves by adding our method to a general activity detection framework using the ActEV/VIRAT dataset.