ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation

TL;DR

ACDnet is a real-time, resource-efficient action detection network for edge devices that leverages flow-guided feature approximation and memory aggregation to maintain high accuracy and speed.

Contribution

The paper introduces ACDnet, a novel compact action detection network that efficiently exploits temporal coherence and memory aggregation for real-time edge computing.

Findings

Achieves over 75 FPS on benchmark datasets.

Maintains competitive accuracy with heavier models.

Demonstrates robustness in real-world scenarios.

Abstract

Interpreting human actions requires understanding the spatial and temporal context of the scenes. State-of-the-art action detectors based on Convolutional Neural Network (CNN) have demonstrated remarkable results by adopting two-stream or 3D CNN architectures. However, these methods typically operate in a non-real-time, ofline fashion due to system complexity to reason spatio-temporal information. Consequently, their high computational cost is not compliant with emerging real-world scenarios such as service robots or public surveillance where detection needs to take place at resource-limited edge devices. In this paper, we propose ACDnet, a compact action detection network targeting real-time edge computing which addresses both efficiency and accuracy. It intelligently exploits the temporal coherence between successive video frames to approximate their CNN features rather than naively extracting them. It also integrates memory feature aggregation from past video frames to enhance current detection stability, implicitly modeling long temporal cues over time. Experiments conducted on the public benchmark datasets UCF-24 and JHMDB-21 demonstrate that ACDnet, when integrated with the SSD detector, can robustly achieve detection well above real-time (75 FPS). At the same time, it retains reasonable accuracy (70.92 and 49.53 frame mAP) compared to other top-performing methods using far heavier configurations. Codes will be available at https://github.com/dginhac/ACDnet.