A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes

Bhaskar Chakraborty*, Michael B. Holte, Thomas B. Moeslund, Jordi Gonzalez, F. Xavier Roca

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

43 Citations (Scopus)

Abstract

Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance.

Original languageEnglish
Pages (from-to)1776-1783
Number of pages8
JournalIEEE International Conference on Computer Vision
Volume(GGS Rating A++, Class 1)
Publication statusPublished - 2011

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