Efficient and Practical Correlation Filter Tracking

Abstract

Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).

Action Recognition, Tracking, and Optimization Analysis of Training Process Based on SVR Model and Multimedia Technology

“…en, under the settings of 400-dimensional features and 1D convolution after inputting fc, directly apply FpN to generate candidate proposals at multiple scales and perform subsequent behavior recognition [19]. Regarding the use of fpN, the thesis experimented with three different multiscale time-series evaluation schemes; among them, SVR1 performs a simple weighted average on the five-scale time-series feature maps obtained from the feature pyramid; SVR2 inputs all five timing characteristic diagrams to the timing evaluation module to fuse the results.…”

Practical Tracking Method based on Best Buddies Similarity

“…Visual tracking is a vital application in bionic robot technology [ 1 – 3 ], which endows robots with the ability to track a specified target by analyzing image data and to autonomously control their motion [ 4 ]. However, visual tracking poses unique challenges for robot vision, as the camera is usually fixed on the robot platform and the target may undergo nonrigid deformation because of the change of perspective caused by the relative motion between the robot and the target [ 5 , 6 ]. It results in dynamic blur and nonrigid deformation, which degrades the performance of the tracker.…”