Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network

TL;DR

This paper introduces a fast single-object tracking method using a fully convolutional neural network that identifies object location in a single shot, achieving high speed and competitive accuracy without heavy matching networks.

Contribution

A novel CNN architecture for single-object tracking that operates in real-time by directly localizing objects with a single forward pass, eliminating the need for complex matching networks.

Findings

Achieves up to 120 FPS on 1080ti hardware.

Performs comparably to state-of-the-art methods in challenging scenarios.

Demonstrates effective object size estimation over time.

Abstract

Object tracking becomes critical especially when similar objects are present in the same area. Recent state-of-the-art (SOTA) approaches are proposed based on taking a matching network with a heavy structure to distinguish the target from other objects in the area which indeed drastically downgrades the performance of the tracker in terms of speed. Besides, several candidates are considered and processed to localize the intended object in a region of interest for each frame which is time-consuming. In this article, a special architecture is proposed based on which in contrast to the previous approaches, it is possible to identify the object location in a single shot while taking its template into account to distinguish it from the similar objects in the same area. In brief, first of all, a window containing the object with twice the target size is considered. This window is then fed into a fully convolutional neural network (CNN) to extract a region of interest (RoI) in a form of a matrix for each of the frames. In the beginning, a template of the target is also taken as the input to the CNN. Considering this RoI matrix, the next movement of the tracker is determined based on a simple and fast method. Moreover, this matrix helps to estimate the object size which is crucial when it changes over time. Despite the absence of a matching network, the presented tracker performs comparatively with the SOTA in challenging situations while having a super speed compared to them (up to $120 FPS$ on 1080ti). To investigate this claim, a comparison study is carried out on the GOT-10k dataset. Results reveal the outstanding performance of the proposed method in fulfilling the task.