# Super-Trajectories: A Compact Yet Rich Video Representation

## TL;DR

This paper introduces super-trajectories, a novel video representation that combines dense trajectory over-segmentation with constraints to reduce tracking errors, enhancing long-term video analysis.

## Contribution

It presents a new compact video representation that maintains long-term pixel tracking information while addressing trajectory tracking errors.

## Key findings

- Provides a more informative video segmentation than traditional superpixels.
- Reduces tracking errors through edge constraints and similarity measures.
- Enhances trajectory-based video analysis applications.

## Abstract

We propose a new video representation in terms of an over-segmentation of dense trajectories covering the whole video. Trajectories are often used to encode long-temporal information in several computer vision applications. Similar to temporal superpixels, a temporal slice of super-trajectories are superpixels, but the later contains more information because it maintains the long dense pixel-wise tracking information as well. The main challenge in using trajectories for any application, is the accumulation of tracking error in the trajectory construction. For our problem, this results in disconnected superpixels. We exploit constraints for edges in addition to trajectory based color and position similarity. Analogous to superpixels as a preprocessing tool for images, the proposed representation has its applications for videos, especially in trajectory based video analysis.