# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 2 Mot Techniques
The visual features are extracted using the fully connected layer (fc8) of the inception network, and the motion feature is a 4-dimensional vector representing the width, height, and relative position from the previous detection. The CTracker framework [43] takes two adjacent frames as inputs and matches each detection with the other using Intersection over Union calculations. A constant velocity motion model is used to match the tracks up to a certain number of frames to handle the reappearance of lost tracks. This approach is end-to-end. It takes two frames as input and outputs two frames with detections and matching tracks. The authors in Ref. [33] introduced an approach in which the detections' dissimilarity measures are computed and then matched. First is the dissimilarity cost computation. Next, the histogram of the H and S channels of the HSV colorspace of the previous detections is compared to the similar histogram of the current detections. A grid structure is used as in Refs. [73; 74]. Hence, multiple histograms are used to match the appearance features. Furthermore, Linear Binary Pattern Histogram (LBPH), introduced in Ref. [75] and used for object recognition in Ref. [76], is utilized for computing the structure-based distance. The predicted and the measured position matching using the L2 norm is added as the motion-based distance. Finally, IoU calculates the size difference between the current detections and the previous tracks. The second step is using the Hungarian algorithm [77] to calculate the overall similarity using the four features calculated in the previous step. The authors in Ref. [68] proposed V-IoU, an extension of IoU [78], for object tracking. The objective here is to reduce the number of ID switches and fragmentation by maintaining the location of lost tracks for a certain number of frames until it appears. A backtracking technique where the reappeared track is projected backward through the frames is implemented to validate that the reappeared track is, in fact, the lost one. In Ref. [46], CenterNet [79] is used to detect objects as points. Centertrack takes two adjacent frames as inputs in addition to the point detections in the previous frame. The tracks are associated using the offset between the current and previous point detections. The authors in Ref. [80] designed a motion segmentation model where the point clusters used for trajectory prediction were placed around the center of the detected bounding box. The approach in Ref. [37] employs optical flow and correlation co-clustering for projecting trajectory points across multiple frames, as illustrated in Figure 7.  
There has been a recent advancement in multiple object tracking and segmentation (MOTS). This field tackles the issues related to the classic MOT, which are associated with the utilization of bounding box detection and tracking, such as background noises and loss of the shape features. The approach introduced in Ref. [81] used an instance segmentation mask on the extracted embedding. The method in Ref. [82] applies contrastive learning for learning the instance-masks used for segmentation. An offline approach introduced in Ref. [83] exploits appearance features for tracking. This method is currently at the top of the leader board at the MOTS20 challenge [84]. The system can achieve more reliability when utilizing multiple sensors to detect and track targets in addition to understanding their intentions [28]. The deep learning approaches are improving and showing promise in the LIDAR datasets. The issue with this is the bad running time, causing difficulty in real-time deployment [29]. The challenges facing 3D tracking are related to the fusion of the data perceived from LIDAR and RGB cameras. Table 3 lists the recent MOT techniques that utilize LIDAR and camera for tracking. [PERSON] et al. [85] proposed complex-YOLO, illustrated in Figure 8, for RGB and LIDAR data detection, tracking, and segmentation. A preprocessing step for the point cloud data input from LIDAR aims to generate a voxalized map of the 3D detections.