# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 2 Mot Techniques
[33], where one network has the current and prior frames as inputs and outputs point tracks. Those tracks are given to a displacement model to measure the similarity. Similarly, another approach to motion modeling is introduced in Ref. [45] to handle overlapping tracks by using the efficient quadratic pseudo-Boolean for optimization. The features extracted from every detection in the current frame must be associated with those extracted from the previous frames. The most popular approach taken for data association in recent years would be the Hungarian algorithm, as in Refs. [50; 53; 60; 63; 67]. The advantage of this method is the accuracy accompanied by a fast computation time. [PERSON] et al. proposed ByteTrack in Ref. [50], where the Kalman filter is used for predicting the detection location followed by two levels of association. The first utilizes the appearance features in addition to the Intersection over Union (IoU) for matching tracks using the  
Figure 4: Track tree proposed in Track tree method system overview. The strength of each branch depends on a score evaluated by the matching algorithm. The green lines indicate matched tracks. The red circle indicate a lost track. Hungarian algorithm. The second level of association deals with the weak detections by utilizing only the IoU with the unmatched tracks remaining from the first level. The authors in Refs. [31; 43; 52] use deep learning networks for data association. The authors in Ref. [39] introduced a model trained using reinforcement learning. The metric learning concept was used to train the matching model in Ref. [32]. The authors in Ref. [62] take advantage of the object detection network for feature extraction. This would save computational costs from applying an appearance feature network on each detection in the current frame. The approaches in Refs. [43; 52] apply the same concept in addition to an end-to-end system that takes the current frame and previous frames as input and outputs the current frame with tracks. The hierarchical single-branch network is an example of an end-to-end system proposed in Ref. [52] as illustrated in Figure 5. The P3 AFormer tracker introduced in Ref. [57] uses the simultaneous detection and tracking framework where a decoder and a detector extract pixel-level features from the current and previous frames. The features are passed into a multilayer perceptron (MLP) that outputs the size, center, and class information. The features are then matched using the Hungarian algorithm. The system overview of the P3 AFormer is shown in Figure 6.  
Figure 5: The hierarchical single-branch network proposed in Ref. [52]. The frames from a video source are passed into the network and outputs detections and tracks. Figure 6: P3 AFormer system overview [57]. The current and previous frames are passed for features extraction and detection. The extraction module consists of a backbone network, pixel-level decoder, and a detector, which is illustrated on the right. The features are passed to MLP heads to output the class, center, and size features. The Recurrent Autoregressive Networks (RAN) approach introduced in Ref. [38] defines an autoregressive model that is used to estimate the mean and variance of appearance and motion features of all associated tracks of the same object stored in the external memory. The internal memory uses the model generated to compare with all upcoming detections. The one with the maximum score and above a certain threshold would then be considered the same object. The external and internal memory would then be updated with the new associated object. There are two types of independence in this approach. The first is that the motion and appearance models include different parameters, so they have different internal and external memories. The second is a new RAN model generated for every new object detected. The lost tracks are terminated after 20 frames.