Tracking using Numerous Anchor points

TL;DR

This paper introduces an online adaptive model-free tracking method that uses anchor-point features and a star graph structure to robustly track objects despite challenges like deformation, occlusion, and scale changes.

Contribution

The paper presents a novel anchor-point feature construction and an adaptive relevance evaluation method for robust, generic, and deformable object tracking without prior object-specific models.

Findings

Demonstrates effectiveness on benchmark datasets

Achieves competitive tracking accuracy

Handles scale variations and occlusions effectively

Abstract

In this paper, an online adaptive model-free tracker is proposed to track single objects in video sequences to deal with real-world tracking challenges like low-resolution, object deformation, occlusion and motion blur. The novelty lies in the construction of a strong appearance model that captures features from the initialized bounding box and then are assembled into anchor-point features. These features memorize the global pattern of the object and have an internal star graph-like structure. These features are unique and flexible and helps tracking generic and deformable objects with no limitation on specific objects. In addition, the relevance of each feature is evaluated online using short-term consistency and long-term consistency. These parameters are adapted to retain consistent features that vote for the object location and that deal with outliers for long-term tracking scenarios. Additionally, voting in a Gaussian manner helps in tackling inherent noise of the tracking system and in accurate object localization. Furthermore, the proposed tracker uses pairwise distance measure to cope with scale variations and combines pixel-level binary features and global weighted color features for model update. Finally, experimental results on a visual tracking benchmark dataset are presented to demonstrate the effectiveness and competitiveness of the proposed tracker.