(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 12, 2020 398 | Page www.ijacsa.thesai.org Compact Scrutiny of Current Video Tracking System and its Associated Standard Approaches Karanam Sunil Kumar 1 Assistant Professor Department of Computer Science and Engineering RNS Institute of Technology, Bangalore, India Dr. N P Kavya 2 Professor Department of Computer Science and Engineering RNS Institute of Technology, Bangalore, India Abstract—With an increasing demands of video tracking systems with object detection over wide ranges of computer vision applications, it is necessary to understand the strengths and weaknesses of the present situation of approaches. However, there are various publications on different techniques in the visual tracking system associated with video surveillance application. It has been seen that there are prime classes of approaches that are only three, viz. point-based tracking, kernel- based tracking, and silhouette-based tracking. Therefore, this paper contributes to studying the literature published in the last decade to highlight the techniques obtained and brief the tracking performance yields. The paper also highlights the present research trend towards these three core approaches and significantly highlights the open-end research issues in these regards. The prime aim of this paper is to study all the prominent approaches of video tracking system which has been evolved till date in various literatures. The idea is to understand the strength and weakness associated with the standard approach so that new approaches could be effectively act as a guideline for constructing a new upcoming model. The prominent challenge in reviewing the existing approaches are that all the approaches are targeted towards achieving accuracy, whereas there are various other connected problems with internal process which has not been considered for e.g. feature extraction, processing time, dimensional problems, non-inclusion of contextual factor, which has been an outcome of the proposed review findings. The paper concluded by highlighting this as research gap acting as contribution of this review work and further states that there are some good possibilities of new work evolution if these issues are considered prior to developing any video tracking system. Overall, this paper offers an unbiased picture of the current state of video tracking approaches to be considered for developing any upcoming model. Keywords—Video tracking; object tracking; visual tracking; video surveillance; object detection I. INTRODUCTION With the advancement of computer vision and video surveillance systems, video tracking has gained immense popularity in both domestic and commercial applications [1]. Fundamentally, video tracking is a mechanism of identifying, recognizing, and tracking a mobile object over time [2]. Apart from its applicability towards video surveillance systems, video tracking is now used over various applications: viz. video editing, medical imaging, traffic control, augmented reality, communication, and video compression, human and computer communication [3-5]. Usually, the comprehensive mechanism of a video tracking system could involve more processing of time owing to its dependency on a massive amount of data within a video sequence [6]. Complexity in the operational process also existing in recognizing an object with accuracy in a video tracking system [7]. Essentially, the video tracking system aims to connect the mobile target object (or multiple objects) present over a sequence of video frames. This could be highly a difficult process, especially when the speed of the mobile object is quite faster relatively or uncertain concerning the defined rate of video frames. The uneven orientation of a mobile object is another complicated scenario in video tracking, which offers complexity in analyzing the presence of an object for a given scene over a sample of time. In order to address this conventional issue, the motion model is adopted in the video tracking system [8]. This motion model is responsible for defining the relationship between the target object image and its influence over the mobility scenario. Regarding the motion model, generally homography or affine transformation is used for two-dimensional models when tracking is performed over planer objects [9]. The motion model for a three-dimensional object is usually related to the position and orientation of the object [10]. While dealing with video compression, the macroblocks are divided into keyframes, and selected motion motions are considered disruptions of these frames considering motion parameters [11]. In the case of a deformable object, the motion model generally considers the position of a target object over the mesh [12]. At present, there is various literature on video tracking systems, which mainly evaluates sequential frames in a video yielding to an identified target object within the transition of frames [13-16]. However, considering the generalized classification, it is found that existing video tracking algorithms are of two types, i.e., representation along with localization of a target object and filtering of data. The first kind of algorithm are generally known for their low computational complexity, and they are again classified into contour-based tracking and kernel-based tracking. The second kind of algorithm mainly deals with the dynamics of the target object and performs assessment based on multiple hypotheses. Thereby, such an algorithm results in enhance capability towards tracking mobile objects of complex form. However, these algorithms are also computationally complex, and it has dependencies over different parameters, e.g., stability, redundancy, quality, etc. The algorithms that fall under such category are Kalman filter and particle filtering. Therefore, the prime research problem considered for this work is that – although there are various implementation and discussion-