Real-Time Visual Tracking through Fusion Features

Yang Ruan,Zhenzhong Wei

doi:10.3390/s16070949

Abstract

Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion.

Highlights

Object tracking is an important computer vision task that has many practical applications, such as security and surveillance, motion analysis, augmented reality, traffic control and human–computer interaction
The tracker that is based on a multi-vector correlation filter (MVCF) that comprises four main components: (1) a scale adjustment that makes all of the elements of a multi-vector feature have the same size; (2) a multi-vector structure that a multi-vector descriptor can be convolved with directly; (3) an update scheme that multiple object appearance models must update separately; 16, 949
We show that our MVCF tracker can obtain substantial performance in multi-object tracking

Summary

Introduction

Object tracking is an important computer vision task that has many practical applications, such as security and surveillance, motion analysis, augmented reality, traffic control and human–computer interaction. A real-time visual tracking system combines software and hardware design. To achieve a smooth output video impression for human eyes, a frame-rate of at least 15 frames per second (FPS) is required. A two-axis turntable will be used to pivot the camera horizontally (yaw) and vertically (pitch). Object tracking will be accomplished through software on the main control computer. An interface allows the user to select a target and to see what the camera is tracking. The system attempts to always keep the object in the center of its field of view. It is noteworthy that target tracking algorithms play a decisive role in this system

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Conclusion