# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 4 Evaluation and Discussion  
In this section, we compare the MOT techniques based on the dataset used for evaluation. Then, analysis and discussion are conducted to provide insight for future work. The performance of the most recent MOT techniques on MOT15, 16, 17, and 20 datasets are shown in Table 5. The \(\uparrow\) stands for higher is better, and \(\downarrow\) stands for lower is better. The protocol indicates the type of detector used for evaluating the results. The dataset provides the public detector and is typical for all methods. The private detector is designed by the method and is not shared. In MOT15, the tracker introduced in Ref. [38] has the highest accuracy and the lowest identity switches (IDs). It also maintained the highest percentage of tracks (MT) and has the lowest percentage of lost ones (ML). On the other hand, it had a significantly higher number of false positives and negatives compared to the method introduced in Ref. [42], which also performed with the highest FPS (Hz). The authors in Ref. [36] evaluated their system using the private detector protocol and have significantly lower fragmentation than all other methods. In Ref. [38], the tracker relied on appearance features extracted from the inception network layer and the position of the detections. The association process was done using conditional probability. The approach in Ref. [48] has the second best accuracy, where the motion features and the appearance features were used, as well as a category classifier for the association. The method with the lowest accuracy [80] only relied on the motion features, and the slowest performing method [39], where reinforcement learning was applied for data association. The approach in Ref. [62] has the highest accuracy on the MOT16 dataset using the private detector protocol. The method in Ref. [53] also used the private detector and has the highest HOTA. Both methods relied on appearance features for tracking and incorporated the Hungarian algorithm for matching. Similarly, a significantly faster-performing method [38] with slightly lower accuracy only used the appearance features and a prediction network for the association. The method in Ref. [35] used the Kalman filter for motion feature prediction and the appearance features extracted from the detection network for tracking. This method has a lower accuracy in comparison to other methods. In MOT17, the approach with the highest accuracy [57] has a significantly higher fragmentation than the one introduced in Ref. [64], which only used the motion feature for tracking. The method in Ref. [67] has slightly lower accuracy but with an acceptable FPS. This method used the appearance and motion features in addition to the Hungarian algorithm for matching. The approaches in Refs. [56,58] have an acceptable accuracy where both used Kalman filter for motion tracking and Ref. [56] neglected the appearance features. The method in Ref. [64] has the lowest number of fragmentation and only uses the motion features in tracking. The method in Ref. [57] has the highest accuracy on MOT20, although it has a significantly higher number of fragmentation compared to the one in Ref. [54]. The approach in Ref. [67] has the highest FPS, followed by the one in Ref. [54]. All of these methods relied on visual and motion features for tracking. The methods in Refs. [58,67] only used the motion features and had an acceptable accuracy. On the other hand, the ones that only relied on the visual features, such as Refs. [60,62,63,65] did not perform well according to the accuracy and other metrics. This evaluation shows that appearance features are essential for high accuracy, and other cues are used to boost performance. Based on the results from Table 5 and the summary presented in Table 2, the utilization of deep learning for data association reduces the processing time, as can be observed from Refs. [43,49]. On the other hand, including motion cues in the system drops the FPS significantly compared to only using the visual features as indicated by the results in Ref.