# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 1 Introduction  
Integrating computer vision and deep learning-based systems in the robotics field has led to a massive leap in the advancement of autonomous feature. The utilization of different sensors, such as cameras and LIDARs, and the progress established by the recent research on processing this data, have introduced multiple object tracking techniques in autonomous driving and robotics navigation systems. Multiple object tracking has been one of the most challenging topics researched through computer vision techniques. The reasons behind this are due to: (1) multiple object tracking (MOT), an essential tool that can be used in enhancing security and automating robotics navigation, and (2) occlusion, which is the main obstacle standing in the path of reaching a reliable accuracy and one issue that is difficult to tackle. In this paper, we aim to survey the different approaches of MOT introduced recently in autonomous robotics.  
Much research has been done to enhance the performance of tracking in SLAM applications [1; 2]. It is simply difficult to navigate through the environment while the positions of the robot itself and other objects in the surrounding are neglected. Tracking is used to estimate the relative location of the robot to other components in the environment. The most challenging part of this process is the existence of highly dynamic objects [3] such as people or vehicles. SLAM-based autonomous navigation in robots has been regarded as essential in development and research primarily because of its potential in many aspects. One example is the autonomous wheelchair systems reviewed in Refs. [4; 5]. The authors in Ref. [6] provided a survey of the mobile devices that assist people with disability. Autonomous driving can cause a reduction in the number of accidents that occur due to fatigue and distractions [7]. Although that might be the case, the public opinion about autonomous vehicles is hesitant about whether to consider the technology trustworthy. Providing awareness and understanding of the capabilities of the sensors in autonomous vehicles to the drivers is vital to reaching the proper employment of the technology in our daily lives. These sensors should not be disregarded or become entirely dependable on them [8]. The approach introduced in Ref. [9] aims to reduce the risks firefighters encounter by deploying a team of UAVs with an MOT system to track wildfires and control the situation. The authors in Ref. [10] employ MOT to guide a swarm of drones and control them. Similarly, MOT is utilized with UAV for collision avoidance in Ref. [11]  
As has been discussed in Refs. [12; 13; 14], the general framework for MOT is shown in Figure 1. The input frame is subjected to an object detection algorithm. Then, the detections from the current frame and the previous frames are used to match the similar trajectories either by motion, appearance, and/or other features. This process would generate tracks presenting the objects through the sequence of frames. Some data association between multiple frames is applied to track an object through multiple frames. A reliable MOT system should be able to handle the new tracks as well as the lost ones. Here, the occlusion issue is where the lost tracks reappear again because they did not move out of the sensor's view but were hidden by other objects.