# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 2 Mot Techniques

Table 2: Summary of the components used in MOT techniques. \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. [36] & CNN + PCA & & & & \\  & + Correlation Filter & - & Hungarian Algorithm & Online & APCE + IoU \\ \hline [PERSON] et al. [48] & Faster-RCNN (VGG16) & - & Category Classifier & Online & Track history \\ \hline [PERSON] et al. [31] & VGG16 + LSTM network & LSTM network & RNN & Online & Track history \\ \hline [PERSON] et al. [49] & Encoder & - & Decoder & Online & Track history \\ \hline [PERSON] et al. [50] & FairMOT & Kalman Filter & Hungarian Algorithm & Online & Track history \\ \hline [PERSON] et al. [51] & LDAE & Kalman Filter & Cosine Distance & Online & Track History \\ \hline [PERSON] et al. [52] & Faster-RCNN (ResNet50) & Kalman Filter & Faster RCNN (ResNet50) & Online & Track history \\ \hline [PERSON] et al. [53] & DLA-34 + CCD + CTE & - & Hungarian Algorithm & Online & Track history \\ \hline [PERSON] et al. [54] & PCB & Kalman Filter & RTU++ & Online & Track history \\ \hline [PERSON] et al. [55] & ResNet34 & - & Depth wise Cross Correlation & Online & Track history \\ \hline [PERSON] et al. [56] & - & Kalman Filter & Normalized IoU & Online & Track history \\ \hline [PERSON] et al. [57] & Transformer + Pixel Decoder & Kalman Filter & Cosine Distance & Online & Track history \\ \hline [PERSON] et al. [58] & FastReID’s SB5-50 & Kalman Filter & Cosine Distance & Online & Track history \\ \hline [PERSON] et al. [59] & ResNet50 & Linear Model & Histogram Distance & Online & Track history \\ \hline [PERSON] et al. [60] & CNN & - & Hungarian Algorithm & Online & Track history \\ \hline [PERSON] et al. [61] & FairMOT & Kalman Filter & IoU + Hungarian Algorithm & Online & Track history \\ \hline [PERSON] et al. [62] & FairMOT & - & Sparse Graph + Hungarian & Online & Track history \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. \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. [63] & DETR Network & - & Hungarian Algorithm & Online & Track history \\ \hline \multirow{3}{*}{[PERSON] et al. [64]} & \multirow{3}{*}{-} & Kalman Filter & \multirow{3}{*}{Observation Centric Recovery} & \multirow{3}{*}{Online} & \multirow{3}{*}{Track history} \\  & & & + non-linear model & & \\  & & + Smoothing & & & \\ \hline [PERSON] et al. [65] & YOLOx & - & Self and Cross Attention Layers & Online & Track history \\ \hline [PERSON] et al. [66] & - & NST Kalman Filter + ResNet50 & AFLink & Online & Track history \\ \hline [PERSON] et al. [67] & ResNet50 & Kalman Filter & Hungarian Algorithm & Online & Track history \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conf. The general framework illustrated in Figure 1 is followed by most of the recent MOT techniques. The most common approach for extracting the visual features of an object is by using CNN. VGG-16 is very popular for this application, as in Refs. [32; 42; 48]. The issue with deploying CNN is the slow computation time due to the high dimensionality output. [PERSON] et al. [36] tackled this issue by applying PCA followed by a correlation filter for dimensionality reduction to the output of the CNN. An encoder with fewer parameters is introduced in Ref. [49] for faster computation. Another popular network for extracting appearance features is ResNet-50, as in Refs. [35; 41; 43], which resulted in a competing accuracy with fast computation. [PERSON] et al. [43] extracted the appearance features from different layers of a ResNet-50 network forming a Feature Pyramid Network, which has the advantage of detecting objects at different scales. The LSTM network is an important concept for architecture design for processing a sequence of movies. It has been used for MOT application in multiple approaches such as Refs. [31; 32].