Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network

TL;DR

This paper introduces a weakly supervised anomaly detection method in surveillance videos using Two-Stream I3D Convolutional Networks and Multiple Instance Learning, achieving higher accuracy with less manual annotation.

Contribution

It presents a novel framework combining Two-Stream I3D networks with MIL for improved, scalable anomaly detection in surveillance videos, reducing reliance on manual labels.

Findings

Outperforms traditional 3D CNNs in accuracy

Reduces manual annotation requirements

Sets new benchmarks in anomaly detection performance

Abstract

The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the field of anomaly detection through the application of Two-Stream Inflated 3D (I3D) Convolutional Networks. These networks substantially outperform traditional 3D Convolutional Networks (C3D) by more effectively extracting spatial and temporal features from surveillance videos, thus improving the precision of anomaly detection. Our research advances the field by implementing a weakly supervised learning framework based on Multiple Instance Learning (MIL), which uniquely conceptualizes surveillance videos as collections of 'bags' that contain instances (video clips). Each instance is innovatively processed through a ranking mechanism that prioritizes clips based on their potential to display anomalies. This novel strategy not only enhances the accuracy and precision of anomaly detection but also significantly diminishes the dependency on extensive manual annotations. Moreover, through meticulous optimization of model settings, including the choice of optimizer, our approach not only establishes new benchmarks in the performance of anomaly detection systems but also offers a scalable and efficient solution for real-world surveillance applications. This paper contributes significantly to the field of computer vision by delivering a more adaptable, efficient, and context-aware anomaly detection system, which is poised to redefine practices in urban surveillance.