# A Visual Object Tracking Algorithm Based on Improved TLD
## 5 Conclusions  
TLD provides an effective framework for solving long-term tracking problems. However, its practical applications are limited due to the complicated calculations and poor real-time performance. To improve the robustness and tracking speed of TLD, several steps were made in this study. Firstly, the tracking module was implemented by the KCF tracker based on HOG descriptors. Secondly, failure detection was added to check if KCF tracks the object correctly. Finally, the detection strategy was optimized. Local detection was carried out to reduce the detecting windows, so as to improve the tracking speed. Full-frame detection is not necessary until the KCF fails to track an object. Comparative experiments were carried out on the benchmark to evaluate the improved algorithm. Comparing the results of TLD with other advanced tracking algorithms showed that the proposed method provides  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Frames**} & \multirow{2}{*}{\begin{tabular}{c} **Frame** \\ **Size** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **TLD** \\ **Tracked** \\ **Frames** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Our** \\ **Tracked** \\ **Frames** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **FPS of** \\ **TLD** \\ **(frames/s)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **FPS of** \\ **Ours** \\ **of FPS** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Multiples** \\ **of FPS** \\ \end{tabular} } \\ \hline Dudek & 1145 & \(720\times 480\) & 1142 & 1145 & 7.14 & 16.67 & 2.35 \\ CarScale & 252 & \(640\times 272\) & 219 & 252 & 6.56 & 14.49 & 2.21 \\ Girl & 500 & \(128\times 96\) & 465 & 481 & 18.14 & 26.32 & 1.45 \\ Redteam & 1000 & \(352\times 240\) & 936 & 1000 & 18.10 & 25.83 & 1.44 \\ CarDark & 389 & \(320\times 240\) & 369 & 389 & 7.01 & 16.36 & 2.34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of successfully tracked frames and frames per second (FPS).  
improvements in terms of accuracy, robustness, and real-time performance. Further, it achieves outstanding tracking performance compared with the common tracking algorithms.  
Conceptualization, X.Z. and S.F.; methodology, X.Z. and S.F.; software, X.Z.; validation, X.Z., Y.W., and W.D.; investigation, Y.W. and W.D.; data curation, X.Z., Y.W., and W.D.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., S.F., Y.W., and W.D.; supervision, S.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TLD & Tracking-learning-detection \\ KCF & Kernelized correlation filters \\ HOG & Histogram of oriented gradient \\ SITF & Scale-invariant feature transform \\ SURF & Speeded up robust features \\ CSK & Circulant structure kernel \\ DSST & Discriminative scale space tracker \\ FBE & Forward and backward error \\ NCC & Normalized correlation coefficient \\ OTB & Object tracking benchmark \\ OR & Overlap rate \\ AOR & Average overlap rate \\ CLE & Center location error \\ FPS & Frames per second \\ SemiT & Semi-supervised tracker \\ SCM & Tracking via sparsity-based collaborative model \\ DFT & Distribution fields for tracking \\ \end{tabular}