Multi-Scale Based Context-Aware Net for Action Detection

Abstract

We address the problem of action detection in continuous untrimmed video streams, based on the two-stage framework: one stage for action proposals generation and the other for proposals classification and refinement. The context features inside and outside a candidate region (proposal) are critical for classification in action detection. Therefore, effective integration of these features with different scales has become a fundamental problem. We contend that different action instances and candidate proposals may need different context features. To address this issue, we present a novel multiple scales based context-aware net (MSCA-Net) to effectively classify the action proposals for action detection in this paper. For each candidate action proposal, MSCA-Net takes its multiple regions with different temporal scales as input and then generates suitable context features. Based on the “candidate-control” mechanism of LSTM, the proposed MSCA-Net specially adopts the two-branch structure: Branch1 generates multi-scale context features for each candidate proposal, whereas Branch2 utilizes the context-aware gate function to control the message passing. Extensive experiments on THUMOS’14, Charades daily and ActivityNet action detection datasets, demonstrate the effectiveness of the designed structure and show how these context features influence the detection results.