English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Feature representation is important for human action recognition. Recently, Wang et al. [25] proposed dense trajectory (DT) based features for action video representation and achieved state-of-the-art performance on several action datasets. In this paper, we improve the DT method in two folds. Firstly, we introduce a motion boundary based dense sampling strategy, which greatly reduces the number of valid trajectories while preserves the discriminative power. Secondly, we develop a set of new descriptors which describe the spatial-temporal context of motion trajectories. To evaluate the performance of the proposed methods, we conduct extensive experiments on three benchmarks including KTH, YouTube and HMDB51. The results show that our sampling strategy significantly reduces the computational cost of point tracking without degrading performance. Meanwhile, we achieve superior performance than the state-of-the-art methods by utilizing our spatial-temporal context descriptors.

Exploring Motion Boundary based Sampling and Spatial-Temporal Context Descriptors for Action Recognition