Real Time Detection Free Tracking of Multiple Objects Via Equilibrium Optimizer

TL;DR

This paper introduces a real-time, detection-free multi-object tracking framework using the equilibrium optimizer to reduce computational complexity and improve tracking accuracy by modeling objects with color histograms.

Contribution

The novel framework combines equilibrium optimizer with reduced-resolution object modeling for efficient, detection-free multi-object tracking.

Findings

EO-based tracker achieves better accuracy than existing methods.

The approach operates in real-time with lower computational cost.

Experimental results validate the effectiveness of the proposed method.

Abstract

Multiple objects tracking (MOT) is a difficult task, as it usually requires special hardware and higher computation complexity. In this work, we present a new framework of MOT by using of equilibrium optimizer (EO) algorithm and reducing the resolution of the bounding boxes of the objects to solve such problems in the detection free framework. First, in the first frame the target objects are initialized and its size is computed, then its resolution is reduced if it is higher than a threshold, and then modeled by their kernel color histogram to establish a feature model. The Bhattacharya distances between the histogram of object models and other candidates are used as the fitness function to be optimized. Multiple agents are generated by EO, according to the number of the target objects to be tracked. EO algorithm is used because of its efficiency and lower computation cost compared to other algorithms in global optimization. Experimental results confirm that EO multi-object tracker achieves satisfying tracking results then other trackers.