Tracking humans using computer vision techniques is important in many surveillance applications. Apart from accuracy and speed, how tracking handles diverse settings such as normal and occluded scenarios also play a critical role. Detection-based tracking is discussed in this study. In this paper, the Multiple Object Tracker is implemented using the tracking-by-detection methodology. Detection is accomplished using the YOLOV8 model, and tracking is performed by various algorithms such as Centroid tracking, Kernelized Correlation Filter (KCF) tracking, CamShift tracking, and Channel and Spatial Reliability Tracker (CSRT) tracking. After performing the tracking by the different tracking algorithms, their performances are analyzed based on different videos. Moreover, in order to keep track of the number of people in the video, unique IDs are assigned to each person based on the IOU distance within the present and previous frames of the video. Besides that, in many edge cases such as occlusion scenarios, the system should be able to maintain the object identity efficiently to keep the count of the number of persons present in the video.