Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking

TL;DR

This paper introduces PAWSS, a novel visual tracking framework that effectively handles scale variations and background interference using patch-based adaptive weighting, segmentation, and multi-scale sampling, achieving superior real-time performance.

Contribution

The paper proposes a new tracking method combining segmentation and multi-scale sampling to improve robustness against scale changes and background noise.

Findings

Outperforms recent state-of-the-art trackers on OTB dataset

Ranks among top trackers on VOT datasets at real-time speeds

Improves successful rate score on OTB dataset

Abstract

Tracking-by-detection algorithms are widely used for visual tracking, where the problem is treated as a classification task where an object model is updated over time using online learning techniques. In challenging conditions where an object undergoes deformation or scale variations, the update step is prone to include background information in the model appearance or to lack the ability to estimate the scale change, which degrades the performance of the classifier. In this paper, we incorporate a Patch-based Adaptive Weighting with Segmentation and Scale (PAWSS) tracking framework that tackles both the scale and background problems. A simple but effective colour-based segmentation model is used to suppress background information and multi-scale samples are extracted to enrich the training pool, which allows the tracker to handle both incremental and abrupt scale variations between frames. Experimentally, we evaluate our approach on the online tracking benchmark (OTB) dataset and Visual Object Tracking (VOT) challenge datasets. The results show that our approach outperforms recent state-of-the-art trackers, and it especially improves the successful rate score on the OTB dataset, while on the VOT datasets, PAWSS ranks among the top trackers while operating at real-time frame rates.