## 2 Literature Review  
Considering the subject's importance, many cohorts across the globe have worked on object tracking algorithms and used them in different applications. This section covers the prominent findings of research activities carried out in the relevant field. [PERSON] and [PERSON] [9] used a particle filtering approach for monitoring objects in a video sequence. They used six parameters model for modeling motion edge. They determined the velocity of the moving edge using spatio-temporal filtering techniques. The multi-dimensional posterior density was used to distribute the filtering of particles over time. They proposed using sampling and sample impoverishment techniques to avoid particles' degeneracy leading to particle-filter convergence failure. Their study has a limitation that the current implementation is manual and involves user interaction relying on some video sequence information, including edge orientation, to collect the correct motion cube. Similarly, [PERSON] et al. [10] proposed an online surveillance algorithm using particle filtering. Their methodology was that a particle filtering-based framework developed for online anomaly detection finds video frames with anomalous activities from the posterior probability of activities present in a video sequence. They proposed that the anomaly detection algorithm was better than other contemporary algorithms in terms of reduced equal error rate (EER)and processing time. [PERSON] and [PERSON] [11] have proposed a real-time tracker system. The operational mechanism of the tracker system was based on the integrated use of color, texture, and movement information. They exploited RGB color histogram and correlogram as color cues using local binary patterns (LBP) to represent texture properties. After extraction of all features, including texture-based background subtraction, they built a unified distance measure. Their unified system worked well on indoor and outdoor control videos compared to a single feature-based system. The proposed system performed satisfactorily under the low lighting and low frame rates conditions, typically in large-scale monitoring systems. The use of a flexible set of indicators makes it less color sensitive. Their proposed system showed robust performance. [PERSON] et al. [12] presented a 3D multi-object-tracking algorithm by establishing the track-to-detection correspondence. The findings of their study compared with challenging datasets reported that their proposed approach accomplished outclass performance. [PERSON] [13] introduced three video control activities: firstly, he implemented a method of human detection to boost an overall human detector for a video using an unsupervised learning framework to train a particular video classifier; secondly, he defined a robust semi-crowded scene tracking by detection system to improve the overall tracking performance using a partial model to manage the appearance changes, and partial occlusions; finally, he implemented a real-time tracking device in the high-resolution video for single target tracking. He also introduced a new form of multiple human segmentation in videos that used partial detection in addition to the video's space-time detail. [PERSON] et al. [14] introduced a model for segmentation of humans in videos using a deep convolutional neural network for human activity monitoring by generating a high-quality mask for human segmentation. Their study reported a better possibility of results in terms of automatically recognizing and segmenting objects in videos. [PERSON] et al. [15] proposed a robust hybrid tracker system that combined deep features with color features forming a hybrid of features. The notion of this technique was de-noising the image using Gaussian smoothing and cropping the irrelevant areas. [PERSON] et al. [16] proposed a novel deblurring approach by adding residual dense blocks into recurrent neural network (RNN) cells to efficiently extract the current frame's spatial features.