This paper addresses recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. Building upon this key observation, we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this temporal self-similarity descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating its high stability under view changes. Self-similarity descriptors are also shown to be stable under performance variations within a class of actions when individual speed fluctuations are ignored. If required, such fluctuations between two different instances of the same action class can be explicitly recovered with dynamic time warping, as will be demonstrated, to achieve cross-view action synchronization. More central to the current work, temporal ordering of local self-similarity descriptors can simply be ignored within a bag-of-features type of approach. Sufficient action discrimination is still retained in this way to build a view-independent action recognition system. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multiview correspondence estimation. Instead, it relies on weak geometric properties and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public data sets. It has similar or superior performance compared to related methods and it performs well even in extreme conditions, such as when recognizing actions from top views while using side views only for training.

This paper deals with the temporal synchronization of image sequences. Two instances of this problem are considered: (a) synchronization of human actions and (b) synchronization of dynamic scenes with view changes. To address both tasks and to reliably handle large view variations, we use self-similarity matrices which remain stable across views. We propose time-adaptive descriptors that capture the structure of these matrices while being invariant to the impact of time warps between views. Synchronizing two sequences is then performed by aligning their temporal descriptors using the Dynamic Time Warping algorithm. We present quantitative comparison results between time-fixed and time-adaptive descriptors for image sequences with different frame rates. We also illustrate the performance of the approach on several challenging videos with large view variations, drastic independent camera motions and within-class variability of human actions.