An Efficient and Robust System for Multiperson Event Detection in Real-World Indoor Surveillance Scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multiperson event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns for multiperson events in the video. To alleviate the need for fine-grained annotation, we propose the use of labeled latent Dirichlet allocation, a weakly supervised method that allows the use of coarse temporal annotations, which are much simpler to obtain. This novel system is able to run at ~10 times real time, while preserving state-of-the-art detection performance for multiperson events on a 100-h real-world surveillance data set (TRECVid surveillance event detection).