## 4 Results and Discussion
### Quantitative Results  
Quantitative data are defined as that can either be counted or compared on a numeric scale. In contrast, its analysis has important implications for data sparsity, generally encountered in representation learning-based environments, for studying scarce and random tracking processes. The quantitative results for both offline and online methods have been illustrated in Tables 1 and 2 for public detectors, respectively. These detectors generate frames based on DPM, FRCNN, and SDP. In Table 1, results with offline methods are listed where the up arrow shows that the higher the value, the better it is, and the down arrow shows that the lesser the value, the better it is. The optimization technique used for MOT is of two types: offline and online versions. The offline MOT, based on training before tracking using past and future frames simultaneously forming a tracking path, performs object detection in the spatial domain by a pre-trained hypothesis in still images, and another pre-trained classifier (offline) is used for object association in the time domain. Similarly, the online version of MOT, based on training while tracking, uses contiguous frame information [64]. Figure 8: The result of applying the proposed tracker on MOT 16-11. Figure 7: The proposed model (SCT) results using MOT 16-09. Figure 9: The proposed model (SCT) results using MOT 16-11. In Table 2, results with online methods are listed where the up arrow shows that the higher the value, the better it is, and the down arrow shows that the lesser the value, the better it is. A synthetic image set is generated from the first frame using the object of interest and is further used for the training of the online trackers. In the next frame, the online trackers detect the object of interest in the subsequent frames. The final frames in the video sequence are used to update the detector online. The bold text represents the best results found in the same performance measuring category. In offline methods, Lifted Multicut and Person Re-identification LMPR [65] for tracking multiple people performed outclass compared to other offline methods in _MOTA_, _IDF1_, _FN_, and _IDSw_. Quadruplet Convolution Neural Network (Quad-CNN) [66] for multiobject tracking achieves lower _FP_ than the other methods. Tracktor [31] performed better in online methods than other competing methods in _MOTA_, _FP_, and _FN_. Dual Matching Attention Networks (DMAN) [32] for detection of the multiobjects has the highest _IDF1_ 54.8, and Spatial-Temporal Attention Mechanism (STAM) [33] achieves minimum IDSs. Similarly, Tables 3 and 4 show the results of private detectors with offline and online methods that use any technique of choice for tracking purposes, respectively. In Table 3, offline methods are listed that assess the trajectory based on past and future observations. Table 4 lists online methods that assess the trajectory based on past to current frames' information. The optimum results are illustrated in Tables 3 and 4 for offline and online methods. and _FN_ compared to the other offline methods. In online methods, our proposed tracker outperformed the other methods like Early Association Multi-Target Tracking EAMTT [36], Deep Simple Online and Realtime Tracking (DeepSORT) [37], CNN Multi-target tracking (CNNMTT) [38], and CTracker [46] in _IDF1_, _MOTA_, _FP_, and _FN_. It has been concluded from the previous results that our tracker generally outperforms all the other detectors in _MOTA_, _FP_, and _FN_, where _IDF1_ increments approximately 7%, _FP_ reduces from 8934 to 2517, _FN_ reduces from 48,305 to 31,405 and _IDSw_ diminishes to half as compared to the CTracker. This concludes the enhancement in results when compared to the previous methods. Initially, the absence of a target object in the previous frame, although appearing in the subsequent frame, then no paired bounding boxes are generated in the \(t\)th frame. When the target object remains present in the subsequent frame, the object is easily identifiable in the future (\(t+1\))th chain node.