## 2 Literature Review
[28] proposed a non-panoramic background modeling technique that modeled each pixel with a single spatio-temporal Gaussian model. The result of the simulations showed that moving the substances was detected with fewer false alarms. This method suffered a serious drawback of insufficient features from the scene. [PERSON] et al. [29] proposed an adaptive pedestrian detection system using image pixel intensities where the foreground objects were isolated from the background. They used a high boost filter to improve the front edges. Similarly, YOLO algorithm park [30] is a real time object tracking method that is known for its speed and accuracy. It is subjected to low recall with high localization error due to insufficient positioning of bounding boxes, and it struggles to detect close or small objects. This problem can be attributed to instability to detect multiple objects that are too close. Tracktor [31] carried out the temporal realignment of the bounding boxes of the object for multiobject tracking manipulating its regression head for the detector. Dual Matching Attention Networks (DMAN) [32] are based on two attention mechanisms: namely spatial- and temporal types, where the former, creating dual attention maps, uses network focus for image-pair (derived through input) by the matching patterns, while the later deemphasizes the noisy details by allocating multi-level attention to samples in the tracklet. Some cohorts worked on the correlation among targets, and occlusion-based drift between them was alleviated using the Spatial-Temporal Attention Mechanism (STAM) [33]. The resultant target map was parameterized, leading to the attention map on a spatial basis achieving MOTA as 46% on the challenging dataset of MOT16. [PERSON] [34] introduced Near-Online Multi-target Tracking (NOMT) framework by computing data covariance sequentially between objects detected in a time window and target frames. The detection due to representation-based learning with high performance resulted in efficient MOT in offline and online systems [35]. [PERSON] et al. [36] introduced a Person of Interest (POI) tracker for an online solution, while the K-Dense Neighbors Tracker (KDNT) for its offline counterpart. The targets and labels are based on the relationship between object localization-path after its prediction. The Early Association Multi Target Tracker (EAMTT) highlights the weak features (only for the existing tracks where detections have lower confidence levels) as well as strong detections (with higher confidence levels) for tracking as well as its initialization. Similarly, [PERSON] et al. [37] introduced Simple Online and Real-time Tracking (SORT) as an extension to the previous work. They used transfer learning-based association merging it as appearance-features information, thereby allowing stranded occlusion bursts reducing identity switch cardinality. [PERSON] et al. [38] pointed out multi-target trackingbased on CNN (CNNMTT) used frames at (t-1 interval) group-wise for describing the useful cross-correlation between discriminative appearance-features working as a good grouping technique for online \(\mathbb{R}^{2}\)-environments without any prior camera calibration information. Recently, a unified tracking graph representation was proposed that combined detections and tracks in one graph and improved tracking performance by providing an online method of tracking objects in 3D [39]. Another method, Motion-Aware Tracker (MAT), was introduced by [PERSON] et al. [40] as a plug-and-play way out with the sole objective of motion-based prediction, including association and reconnection. Tracking-by-detection (TBD) [41] was proposed, which localized the pedestrian in each frame and connected object hypotheses into the trajectories without initial labeling. These methods are mostly dependent on the accuracy and data association of objects. Another framework, namely CenterPoint, was proposed by [PERSON] et al.