Learning Temporal Alignment Uncertainty for Efficient Event Detection

Abbasnejad, Iman, Sridharan, Sridha, Denman, Simon, & Fookes, Clinton B. (2015) Learning Temporal Alignment Uncertainty for Efficient Event Detection. In International Conference on Digital Image Computing: Techniques and Applications (DICTA), 23-25 November 2015, Adelaide Town Hall, Adelaide, South Australia, Australia.

View at publisher (open access)

Abstract

In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and efficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence. A drawback to the BOW representation, however, is the intrinsic destruction of the temporal ordering information. In this paper we propose a new representation that leverages the uncertainty in relative temporal alignments between pairs of sequences while not destroying temporal ordering. Our representation, like BOW, is of a fixed dimensionality making it easily integrated with a linear detection function. Extensive experiments on CK+, 6DMG, and UvA-NEMO databases show significant performance improvements across both isolated and continuous event detection tasks.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 90802
Item Type: Conference Paper
Refereed: Yes
Keywords: Event Detection, Dynamic Time Warping, Temporal Alignment, Bag of Words
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: The authors.
Deposited On: 26 Nov 2015 23:52
Last Modified: 15 Feb 2016 00:36

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page