When NAS Meets Anomaly Detection: In Search of Resource-Efficient Architectures in Surveillance Video

Haidong Kang, Lianbo Ma, Nan Li, Jianlun Ma, Jian Cheng

Abstract: Recently, visual sensors have been deployed almost everywhere, generating substantial volumes of surveillance video data in smart cities. Anomaly detection aims to detect anomalous events for smart surveillance video analytics. However, exsiting methods rely on manually designed architectures to learn and extract feature representations, which is inefficient and labor intensive. To address this bottleneck, in this work, we propose a Neural Architecture Search (NAS) method (AdNAS), which aims to automatically find optimal architecture for anomaly detection in surveillance video. First, we design search space for anomaly detection task, which contains various convolutional operators and a weight-sharing supernet. Moreover, we we present a differentiable strategy to facilitate the deployment of latency-aware architectures across a range of edge platforms in an end-to-end manner. Extensive experiments show that our AdNAS achieves competitive performance in several benchmarks, especially achieving highest mean Average Precision (mAP) on the fire dataset-1 for anomaly detection task.