# ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect
## 2 Literature Survey  
In this section, we discuss the literature survey related to the design of human detection and tracking systems. [PERSON] et al. [3] proposed a human detection module that is responsible for invoking the detection algorithm. Ideally, the detection algorithm is to be run on each input frame. However, this will inhibit the system from meeting its real-time requirements. Instead, the detection algorithm in their implementation is invoked every two seconds. The location of the human targets at the remaining time is determined by tracking the detected humans using the tracking algorithm. To further speed up the process, the detection algorithm does not look for humans in the entire frame. Instead, it looks for humans in the regions determined to be foreground regions. The tracking module processes frames and detections received from the human detection module to retain information about all the existing tracks. When a new frame is received, the current tracks are extended by locating the new bounding boxes for each track location in the new frame. If the received frame is accompanied with new detections, the new detections are compared to the current tracks. If the new detection significantly overlaps with one of the existing tracks, it is ignored. Otherwise, the new track is created as a new detection. A track is terminated if the tracking algorithm fails to extend it in a new input frame. [PERSON] et al. [4] proposed a human detection and tracking system consisting of seven modules: Data collector (DC) module, shape detector (SD) module, location decision (LD) module, shape standardization (SS) module, feature collector (FC) module, motion decision (MD) module, and a location recognition (LR) module. The DC module acquires color images from the digital network cameras every three seconds. The SD module analyzes the human's volume from four images, which is acquired from the DC module. They used a moving window to extract the human's coordinates in each image. The LD module analyses the user's location through coordinates that are offered by the SD module. The LD module also analyses whether the human is near to which home appliance through a comparison between the absolute coordinate of the human and the absolute coordinate of the appliance in the home. The SS module converts the image into a standard image for recognition of multi-users' motion. The FC module extracts the image that is changed by the SS module, and the MD module predicts the motion of the detected human. The SD module receives raw images from the DC module to distinguish the human's image through subtraction between the second image and the third image, then calculates the human's absolute coordinates in the home and the human's relative coordinates with reference to the furniture and home appliances. The SD module also decides which furniture the human is near to. If there is a human in an important place that is defined for the human tracking agent, the SD module calculates relative coordinates of the moving window, which includes a human, and transmits those to the LD module. The LR module can judge a user's location without conversion of the acquired image. Unlike the human's location, the human's motion should be recognized to differ in the case of each person because a person's appearance differs from each other. The SS module takes charge of motion recognition in the multi-user situation. To recognize the human's motion, they defined feature sets of six standardized motion showing six motions that are recognized in the human tracking. [PERSON] et al. [5] proposed a human tracking system based on video cameras. There are three steps in the proposed system. The first step is a Gaussian-based background subtraction process [6] to remove the background of the input image.