Temporal-Needle: A view and appearance invariant video descriptor

TL;DR

The paper introduces the 'temporal-needle' video descriptor that captures dynamic behavior invariant to viewpoint and appearance, enabling improved action detection, alignment, and clustering across diverse videos.

Contribution

It proposes a novel multi-scale, self-similarity based descriptor that is invariant to viewpoint and appearance, and introduces a method for efficient matching using statistically significant descriptors.

Findings

Effective in detecting similar actions across videos with different viewpoints and backgrounds.

Useful for temporal/spatial alignment and unsupervised video clustering.

Demonstrated on stationary camera videos, with potential extension to moving cameras.

Abstract

The ability to detect similar actions across videos can be very useful for real-world applications in many fields. However, this task is still challenging for existing systems, since videos that present the same action, can be taken from significantly different viewing directions, performed by different actors and backgrounds and under various video qualities. Video descriptors play a significant role in these systems. In this work we propose the "temporal-needle" descriptor which captures the dynamic behavior, while being invariant to viewpoint and appearance. The descriptor is computed using multi temporal scales of the video and by computing self-similarity for every patch through time in every temporal scale. The descriptor is computed for every pixel in the video. However, to find similar actions across videos, we consider only a small subset of the descriptors - the statistical significant descriptors. This allow us to find good correspondences across videos more efficiently. Using the descriptor, we were able to detect the same behavior across videos in a variety of scenarios. We demonstrate the use of the descriptor in tasks such as temporal and spatial alignment, action detection and even show its potential in unsupervised video clustering into categories. In this work we handled only videos taken with stationary cameras, but the descriptor can be extended to handle moving camera as well.