CUED Publications database

Part Bricolage: Flow-assisted part-based graphs for detecting activities in videos

Shankar, S and Badrinarayanan, V and Cipolla, R (2014) Part Bricolage: Flow-assisted part-based graphs for detecting activities in videos. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8694 L. pp. 586-601. ISSN 0302-9743

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Space-time detection of human activities in videos can significantly enhance visual search. To handle such tasks, while solely using low-level features has been found somewhat insufficient for complex datasets; mid-level features (like body parts) that are normally considered, are not robustly accounted for their inaccuracy. Moreover, the activity detection mechanisms do not constructively utilize the importance and trustworthiness of the features. This paper addresses these problems and introduces a unified formulation for robustly detecting activities in videos. Our first contribution is the formulation of the detection task as an undirected node- and edge-weighted graphical structure called Part Bricolage (PB), where the node weights represent the type of features along with their importance, and edge weights incorporate the probability of the features belonging to a known activity class, while also accounting for the trustworthiness of the features connecting the edge. Prize-Collecting-Steiner-Tree (PCST) problem [19] is solved for such a graph that gives the best connected subgraph comprising the activity of interest. Our second contribution is a novel technique for robust body part estimation, which uses two types of state-of-the-art pose detectors, and resolves the plausible detection ambiguities with pre-trained classifiers that predict the trustworthiness of the pose detectors. Our third contribution is the proposal of fusing the low-level descriptors with the mid-level ones, while maintaining the spatial structure between the features. For a quantitative evaluation of the detection power of PB, we run PB on Hollywood and MSR-Actions datasets and outperform the state-of-the-art by a significant margin for various detection paradigms. © 2014 Springer International Publishing.

Item Type: Article
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:16
Last Modified: 10 Aug 2017 01:37