Efficient Early Event Detector for Streaming Sequence

Abstract

Extensive research has been paid for event detection in the past decades. However, the timeliness requirement of real-world applications cannot be satisfied by these approaches. Early event detector is thus proposed recently to deal with this issue. Early detection aims to recognize the target as early as possible, i.e., it can detect partial events and create a monotonous function to rank them. Although important and practical, few studies have been given for early detection due to its complexity. Max-margin Early Event Detector (MMED) is a well-known approach, which achieves satisfied performance in identifying partial events. However, the MMED works in an offline manner and may fail in this era of streaming sequence. In addition, the large memory consumption and high retraining time cost of the MMED are hard to be satisfied in general platform conditions. In this paper, we introduce an online learning technique with max-margin to early event detection. The proposed model could be adapted to the changing data distribution of the streaming sequences. No historical data need to be stored. Therefore, both the memory requirement and retraining time cost are decreased significantly. We evaluate the proposed approach on three benchmark datasets with various complexities. The extensive results demonstrate both the effectiveness and efficiency of the proposed framework.