## 1 Introduction  
Object video tracking is important for many applications for cohorts working in the computer vision arena, including video and security surveillance, traffic control, video communication, robotics and animation, video sensor targeting, robotics, human computing, etc. Several object-tracking methods are used in video sequencing to identify moving objects. Multiple Object Tracking (MOT), based on computer vision, has many important implications related to object recognition, belonging to many categories, like chairs, glasses, cars, and pedestrians, and its tracking without its posterior details about the shape characteristics, including the objects' count [1]. MOT is a very complicated task that is difficult to solve; therefore, we need to develop robust methods for object detection [2]. A gap exists to accommodate smooth but reasonable tracklets by reducing ID switches, assigning IDs to wrong objects, and using the tracked trajectories. Keeping only the neighboring framesand consequently raising the speed of object tracking ensures robustness by evaluating the unknown position of the target. The minimum mapping criterion should be selected to raise the speed of OT by avoiding misleading tracklets followed by an object recognition system. Convolutional Neural Networks (CNN) are state-of-the-art in spatial pattern extraction and useful in various tasks like image classification [3; 4; 5] and its positional details. Object recognition aims to describe a series of related vision tasks involving activities such as the identification of digital photographic objects. Classifying images requires tasks such as predicting an object's class in an image. Object position refers to the location of an image and the drawing of a box around the extent of one or more objects, and it works by integrating these two tasks by finding one or more objects in an image. The goal of tracking objects in consecutive video frames is the association of target objects. The object's shape, location, and position in the video frames are needed for object tracking. Object detection and classification in computer vision systems are the most important phases in object tracking [6; 7; 8]. Object detection is the first and most important tracking step to identify or locate the moving object in a picture. Subsequently, objects detected could be categorized in terms of cars, persons, swaying trees, birds, and others. Image processing techniques make monitoring objects in consecutive frames difficult or challenging. Different problems may occur due to complex object motion, irregular object form, object-to-object occlusion, and processing requirements in real time. Tracking video objects is an important computer vision research subject. Many algorithms for object tracking have been proposed. However, there are still various problems with tracking objects in the images, such as a change in illumination, occlusion, movement blurring, change in size, change in scale, appearance changes, camera movement, confusing scenes, and so on. Similarly, the interval between two adjacent frames affects target tracking by increasing the inherent frame rate at which the video sequence is acquired and provides extra time for more information in the form of a frame, leading to the loss of information in case the lower frame rate is maintained. In this article, the following aspects are discussed:  
1. The proposed method, the SCT model, based on Kalman filtering and bipartite matching, is presented. It is an online MOT model to optimize feature extraction, object detection, and data association. It performs data association to object detection (pairwise). 2. The informative regions are improved using box pair regression of SCT with the help of a joint attention unit to enhance performance. 3. The proposed SCT reaches remarkable performance with datasets of MOT16 and MOT17. The paper is organized as follows; Section 2: Literature Review, Section 3: Materials and Methods, and Section 4: Results and Discussion, followed by Conclusions.