# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 2 Mot Techniques  
In this section, we go through the most recent MOT techniques and the common trends being followed for matching tracks across multiple frames. Table 2 shows a summary of the components used in MOT techniques. It can be observed that the appearance cue is rarely neglected. Motion cue also shows presence a lot. Most approaches depend on deep learning for extracting visual features. CNNs are vital tools that can extract visual features from the tracks and achieve accurate detections of tracks \begin{table}
\begin{tabular}{c c c} \hline \hline
**Review** & **Year** & **Evaluation Dataset** \\ \hline [PERSON] et al. [13] & 2019 & MOT 15, 16, 17 \\ \hline [PERSON] et al. [14] & 2019 & MOT 15, 16 \\ \hline [PERSON] et al. [12] & 2022 & PETS2009-S2L1 \\ \hline
**Ours** & **2022** & **KITTI, MOT 15, 16, 17, 20, and UA\_DETRAC** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent reviews and the data used for evaluation and comparison. matching [14]. The approaches introduced in Refs. [31; 32] use Long Short Term Memory (LSTM) based networks for motion modeling. LSTM networks are considered in MOT for appearance and motion modeling as they can find patterns by efficiently processing the previous frames in addition to the current ones. On the other hand, The authors in Ref. [33] generated histograms from the detections and used them as the appearance features. As for data association, the Hungarian algorithm is common with MOT techniques, such as Refs. [33; 34; 35; 36], for associating the current detections with the previous ones, although the performance of these techniques did not show much potential. Deep learning has rarely been utilized for data association. However, the best performing technique on MOT16 and MOT17 datasets relied on a prediction network to validate that the two bounding boxes are related. For occlusion handling, most approaches rely on feeding the history of tracks into the tracking system to validate the lost ones. The tracks absent for a specific number of frames would be considered lost and deleted from the history. This is to avoid processing a massive number of detections and reducing the FPS. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tracker** & **Appearance Cue** & **Motion Cue** & **Data Association** & **Mode** & **Occlusion Handling** \\ \hline [PERSON] et al. [37] & - & Optical flow & Correlation Co-Clustering & Online & Point trajectories \\ \hline [PERSON] et al. [38] & fc8 layer of the inception network & Relative center coordinates & Conditional Probability & Online & Track history \\ \hline [PERSON] et al. [32] & VGG-16 & LSTM network & Metric Learning & Online & Track history \\ \hline [PERSON] et al. [39] & CNN & - & Reinforcement Learning & Online & SVM Classifier \\ \hline [PERSON] et al. [40] & ECO & - & Dual Matching & Online & Spatial attention network \\ \hline [PERSON] et al. [41] & ResNet50 & Linear Model & Siamese Networks & Online & Track history \\ \hline [PERSON] et al. [42] & VGG-like network & - & Affinity estimator & - & Track history \\ \hline [PERSON] et al. [43] & ResNet-50 + FFN & - & Prediction Network & Online & Track history \\ \hline [PERSON] et al. [44] & FaceNet & - & Multi-Scale TrackletNet & - & Track history \\ \hline [PERSON] et al. [34] & CNN & \
\begin{tabular}{c} Relative mean \\ velocity and position \\ \end{tabular} & Hungarian Algorithm & Online & Track history \\ \hline [PERSON] et al. [35] & ResNet-50 & Kalman Filter & Hungarian Algorithm & Online & Track history \\ \hline [PERSON] et al. [45] & Decoder & Relative position & Unary Potential & Near-online & Track history \\ \hline [PERSON] et al. [46] & - & CenterTrack & 2D displacement prediction & Online & Track history \\ \hline [PERSON] et al. [33] & Matching histograms & Relative position & Hungarian Algorithm & Online &
\begin{tabular}{c} Grid Structure \\ + Track history \\ \end{tabular} \\ \hline [PERSON] et al. [47] & Siamese-style network & - & RTA Power Iteration layer & Online & Track history \\ \hline \hline \end{tabular}
\end{table}