Title

Correcting Cuboid Corruption For Action Recognition In Complex Environment

Abstract

The success of recognizing periodic actions in single-person-simple- background datasets, such as Weizmann and KTH, has created a need for more difficult datasets to push the performance of action recognition systems. We identify the significant weakness in systems based on popular descriptors by creating a synthetic dataset using Weizmann dataset. Experiments show that introducing complex backgrounds, stationary or dynamic, into the video causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning the system or selecting better interest points. Instead, we show that the problem lies at the cuboid level and must be addressed by modifying cuboids. © 2011 IEEE.

Publication Date

12-1-2011

Publication Title

Proceedings of the IEEE International Conference on Computer Vision

Number of Pages

1540-1547

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCVW.2011.6130433

Socpus ID

84863064000 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84863064000

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