Visual object tracking based on Motion-Adaptive Particle Filter under complex dynamics

Songxiao Cao,Xuanyin Wang,Ke Xiang

doi:10.1186/s13640-017-0223-0

Songxiao Cao, Xuanyin Wang + Show 1 more

Open Access

https://doi.org/10.1186/s13640-017-0223-0

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Abstract

This paper presents a novel particle filter called Motion-Adaptive Particle Filter (MAPF) to track fast-moving objects that have complex dynamic movements. The objective was to achieve effectiveness and robustness against abrupt motions and affine transformations. To that end, MAPF first predicted both velocity and acceleration according to prior data of the tracked objects, and then used a novel approach called sub-particle drift (SPD) to improve the dynamic model when the target made a dramatic move from one frame to the next. Finally, the propagation distances of each direction in the dynamic model were determined based on the results of motion estimation and SPD. Experimental results showed that the proposed method was robust for tracking objects with complex dynamic movements and in terms of affine transformation and occlusion. Compared to Continuously Adaptive Mean-Shift (CAM-Shift), standard particle filter (PF), Velocity-Adaptive Particle Filter (VAPF), and Memory-based Particle Filter (M-PF), the proposed tracker was superior for objects moving with large random velocities and accelerations.

Highlights

Visual object tracking is becoming more and more important in many application fields nowadays, such as surveillance, robots, and human-computer interfaces [1,2,3]
This paper proposed a novel particle filter (PF) called Motion-Adaptive Particle Filter (MAPF), which could predict both velocity and acceleration based on the past data of the tracked object, and introduced a novel method called subparticle drift (SPD) to improve the performance of the tracker during the target’ dramatic motion from one frame to the following
4 Results and discussion To demonstrate the effectiveness and robustness of the proposed tracking scheme, 13 different color videos were used in the experiments (Additional files 1–13), which were obtained from different datasets

Summary

Introduction

Visual object tracking is becoming more and more important in many application fields nowadays, such as surveillance, robots, and human-computer interfaces [1,2,3]. Many algorithms and methods have been proposed to track moving objects in video sequence, among which the mean shift (MS) and particle filter (PF) are frequently used. 1.1 Motion-adaptive problem of tracking Most PF-based trackers use the linear Gaussian dynamic model as their target motion model. This simple model cannot match the complexity of a fast-moving object with random velocity and acceleration. This paper proposed a novel PF called Motion-Adaptive Particle Filter (MAPF), which could predict both velocity and acceleration based on the past data of the tracked object, and introduced a novel method called subparticle drift (SPD) to improve the performance of the tracker during the target’ dramatic motion from one frame to the following. The M-PF did not show any advantage because the video sequence was too short and did not have much history data for constructing a better prior distribution

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Results

Conclusion