# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 2 Mot Techniques
The main approach taken by most current methods is to store the appearance features of the previous frames and retrieve them for comparison with the ones of the current frame. The important factor that affects the reliability of this comparison is the updating of the stored features. The object's appearance varies through the frames but not significantly between two adjacent frames; hence, constant updating can lead to a higher matching accuracy. The second most common feature used for tracking is that related to the object's motion. This is specifically useful at full occlusion occurrence. In this case, the object's state can change significantly, and the appearance features will not be reliable for matching. A robust motion model can predict the object's location even if it disappears from the scene. The most common approach for motion tracking is the Kalman Filter, as in Refs. [50; 51; 52]. The authors in Refs. [34; 45] use the relative position between two tracks in two adjacent frames and decide whether or not the two tracks are of the same object. The authors in Refs. [31; 32; 46], use deep learning approaches for motion tracking. The most common architecture for this approach is the LSTM network. The Kalman filter has a significantly lower computational cost than the deep learning approaches. The issue with utilizing only motion models for tracking is the random motion of objects. For instance, motion models would work better on cars where the motion is limited than on people. [PERSON] et al. introduced CenterTrack in Ref. [46] for tracking objects as points. The system is end-to-end, taking the current and the previous frames and outputting the matched tracks as illustrated in Figure 3.  
Figure 3: The point tracking approach in Ref. [46]. The current and previous frames are passed into the centerTrack network, which utilize the motion feature to detect and match tracks. There are other features used for tracking. The authors in Refs. [56; 61], added the IoU metric between the adjacent frames' detections to match the two tracks. The tracking of one object in the scene can be affected by other objects. For this reason, some methods have introduced the interactivity feature to the tracking algorithm. In Ref. [31], the interactivity features were extracted from an occupancy map using an LSTM network. The authors in Ref. [54] used a tracking graph and designed a set of conditions to measure the interactivity between two objects. Figure 4 illustrates the overview of tracking graph methods. The approach in Ref. [63], exploited the size and structure as features along with the appearance and motion for tracking. Increasing the number of features can improve the tracking process at the cost of computation time. The main issue would be the processing needed to fuse those features with different dimensionalities. The authors in Refs. [44; 68] used IOU in addition to appearance and motion features to increase the reliability of the tracking. The authors in Ref. [44] further improved the model by adding epipolar constraints with the IOU and introduced a tracklenet to group similar tracks into a cluster. Ref. [69] added the deep_sort algorithm to the extracted features to reduce the unreliable tracks, and Ref. [36] added a correlation filter tracker to the CNN. Ref. [47] performed a similar approach of performing feature extraction and matching simultaneously by having affinity estimation and multi-dimensional assignment in one network. The authors in Refs. [70; 71] experimented with 3D distance estimation from RGB frames. In Ref. [70], Poisson multi-Bernoulli mixture tracking filter was used to perform the 3D projections. In addition to CNN, Ref. [72] experimented visually with the track's Gaussian Mixture Probability Hypothesis Density. The authors in Ref. [37] introduced a motion segmentation framework using motion cues in addition to the IOU and bounding box clusters for object tracking through multiple frames. An interesting technique is established in Ref.