Marrying Tracking with ELM: A Metric Constraint Guided Multiple Feature Fusion Method

TL;DR

This paper introduces a multi-view feature fusion approach for object tracking that combines Extreme Learning Machine (ELM) to improve robustness against occlusion and background clutter, outperforming existing methods.

Contribution

It proposes a novel multi-view fusion method guided by metric constraints and integrates ELM to enhance tracking accuracy and efficiency, addressing drift and partial occlusion issues.

Findings

Outperforms state-of-the-art methods on 12 challenging sequences.

Effectively handles occlusion, illumination, and deformation.

Reduces tracking drift with a novel sample selection strategy.

Abstract

Object Tracking is one important problem in computer vision and surveillance system. The existing models mainly exploit the single-view feature (i.e. color, texture, shape) to solve the problem, failing to describe the objects comprehensively. In this paper, we solve the problem from multi-view perspective by leveraging multi-view complementary and latent information, so as to be robust to the partial occlusion and background clutter especially when the objects are similar to the target, meanwhile addressing tracking drift. However, one big problem is that multi-view fusion strategy can inevitably result tracking into non-efficiency. To this end, we propose to marry ELM (Extreme learning machine) to multi-view fusion to train the global hidden output weight, to effectively exploit the local information from each view. Following this principle, we propose a novel method to obtain the optimal sample as the target object, which avoids tracking drift resulting from noisy samples. Our method is evaluated over 12 challenge image sequences challenged with different attributes including illumination, occlusion, deformation, etc., which demonstrates better performance than several state-of-the-art methods in terms of effectiveness and robustness.