Improving Human Annotation in Single Object Tracking

TL;DR

This paper analyzes the limitations of human annotations in video object tracking and proposes a smoothing trajectory method to improve annotation consistency and training data quality.

Contribution

It introduces a novel smoothing trajectory strategy with adaptive image alignment to enhance annotation quality in video tracking.

Findings

Smoothing improves annotation consistency and model training.

The method handles moving scenes effectively.

Over-smoothing can introduce errors.

Abstract

Human annotation is always considered as ground truth in video object tracking tasks. It is used in both training and evaluation purposes. Thus, ensuring its high quality is an important task for the success of trackers and evaluations between them. In this paper, we give a qualitative and quantitative analysis of the existing human annotations. We show that human annotation tends to be non-smooth and is prone to partial visibility and deformation. We propose a smoothing trajectory strategy with the ability to handle moving scenes. We use a two-step adaptive image alignment algorithm to find the canonical view of the video sequence. We then use different techniques to smooth the trajectories at certain degree. Once we convert back to the original image coordination, we can compare with the human annotation. With the experimental results, we can get more consistent trajectories. At a certain degree, it can also slightly improve the trained model. If go beyond a certain threshold, the smoothing error will start eating up the benefit. Overall, our method could help extrapolate the missing annotation frames or identify and correct human annotation outliers as well as help improve the training data quality.