Information Elevation Network for Fast Online Action Detection

TL;DR

This paper introduces the Information Elevation Network (IEN), a novel online action detection model that efficiently models relevant past information using the proposed IEU, outperforming existing methods on benchmark datasets.

Contribution

The paper proposes the IEU for better past information modeling and develops the IEN, the first efficient OAD network considering computational overhead, using only RGB features.

Findings

IEN outperforms state-of-the-art methods on THUMOS-14 and TVSeries datasets.

IEN achieves superior accuracy using only RGB frames.

IEN surpasses methods using two-stream features on THUMOS-14.

Abstract

Online action detection (OAD) is a task that receives video segments within a streaming video as inputs and identifies ongoing actions within them. It is important to retain past information associated with a current action. However, long short-term memory (LSTM), a popular recurrent unit for modeling temporal information from videos, accumulates past information from the previous hidden and cell states and the extracted visual features at each timestep without considering the relationships between the past and current information. Consequently, the forget gate of the original LSTM can lose the accumulated information relevant to the current action because it determines which information to forget without considering the current action. We introduce a novel information elevation unit (IEU) that lifts up and accumulate the past information relevant to the current action in order to model the past information that is especially relevant to the current action. To the best of our knowledge, our IEN is the first attempt that considers the computational overhead for the practical use of OAD. Through ablation studies, we design an efficient and effective OAD network using IEUs, called an information elevation network (IEN). Our IEN uses visual features extracted by a fast action recognition network taking only RGB frames because extracting optical flows requires heavy computation overhead. On two OAD benchmark datasets, THUMOS-14 and TVSeries, our IEN outperforms state-of-the-art OAD methods using only RGB frames. Furthermore, on the THUMOS-14 dataset, our IEN outperforms the state-of-the-art OAD methods using two-stream features based on RGB frames and optical flows.