Continuous action recognition is more challenging than isolated recognition
because classification and segmentation must be simultaneously carried out. We
build on the well known dynamic time warping (DTW) framework and devise a novel
visual alignment technique, namely dynamic frame warping (DFW), which performs
isolated recognition based on per-frame representation of videos, and on
aligning a test sequence with a model sequence. Moreover, we propose two
extensions which enable to perform recognition concomitant with segmentation,
namely one-pass DFW and two-pass DFW. These two methods have their roots in the
domain of continuous recognition of speech and, to the best of our knowledge,
their extension to continuous visual action recognition has been overlooked. We
test and illustrate the proposed techniques with a recently released dataset
(RAVEL) and with two public-domain datasets widely used in action recognition
(Hollywood-1 and Hollywood-2). We also compare the performances of the proposed
isolated and continuous recognition algorithms with several recently published
methods.

本文提出了基于动态时间扭曲 (DTW) 框架的动态帧扭曲 (DFW) 技术，该技术能够对视频进行基于逐帧表示的单独识别，并对测试序列与模型序列进行对齐。并提出了一过程 DFW 和二过程 DFW 两种方法，使其能够与分割同时进行。在 RAVEL 数据集、Hollywood-1 和 Hollywood-2 公共数据集上进行了测试并进行了性能比较。