Spatial-Temporal Pyramid Matching for Crowd Scene Analysis

Abstract

Crowd scene analysis has caught significant attention both in academia and industry as it has a great number of potential applications. In this paper, we propose a novel spatial-temporal pyramid matching scheme for crowd scene analysis. Video segments are represented as concatenated histograms of all cells at all pyramid levels with corresponding weights, which reflect corresponding matches at finer resolutions are weighted more highly than that found at coarser resolution. Using the classical stochastic gradient descent method, we also propose an online one-class support vector machine algorithm for online anomaly detection scenarios. Extensive experiments have been carried out on two benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our approach.