# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
###### Abstract  
Multi-object tracking faces critical challenges, including occlusions, ID switches, and erroneous detection boxes, which significantly hinder tracking accuracy in complex environments. To address these issues, this study proposes a single-task joint learning (STJL) model integrated into an online multi-object tracking framework to enhance feature extraction and model robustness across diverse scenarios. Employing cross-dataset training, the model has improved generalization capabilities and can effectively handle various tracking conditions. A key innovation is the refined tracker initialization strategy that combines detection and tracklet confidence, which significantly reduces the number of false positives and ID switches. Additionally, the framework employs a combination of Mahalanobis and cosine distances to optimize data association, further improving tracking accuracy. The experimental results demonstrate that the proposed model outperformed state-of-the-art methods on standard benchmark datasets, achieving superior MOTA and reduced ID switches, confirming its effectiveness in dynamic and occlusion-heavy environments. multi-object tracking; single-task joint learning; cross-dataset training; feature extraction; tracker initialization; cosine distance; data association; occlusion handling +
Footnote †: journal: applied sciences  
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significant computational demand and lack of real-time capability. By contrast, online methods process frames sequentially, relying on current and past data, making them suitable for real-time applications. However, online tracking faces difficulties with occlusions and fragmented trajectories, which complicate consistent object identification. Network flow [1] models multi-object tracking using a directed cyclic graph to determine the optimal trajectories via a minimum-cost algorithm. [PERSON] et al. [2] combined temporal and spatial data for cross-camera pedestrian tracking, optimized by a conditional random field. [PERSON] et al. [3] improve tracking with proposal generation and graph convolutional networks. [PERSON] et al. [4] streamlined re-ID with energy minimization for dense scenes. Although energy-based methods [5] can achieve global optimization, offline tracking methods are computationally heavy and unsuitable for real-time applications. Multi-object tracking [6] models a problem using Markov decision processes (MDPs). TC-ODAL [7] improves track reliability by classifying trackers based on detectability and continuity, using linking strategies and incremental linear discriminant analysis. SORT [8] enables real-time tracking with the Kalman filter and Hungarian algorithm, enhanced by a faster R-CNN. Although traditional methods rely on visual similarity and feature models to reduce ID switches, they often fail to distinguish subtle differences between similar targets, leading to potential misidentifications in online tracking scenarios. Traditional feature models with manually designed descriptors struggle with noise and imprecise target representations. In contrast, the convolutional neural network (CNN) [9] proposed by [PERSON] learns features autonomously, overcoming these limitations. CD  
learning, based on multi-task learning [18], merges training data from various tasks for common feature representations, enhancing performance in multiple applications [19; 20; 21; 22; 23; 24]. Ref. [25] used a cross-domain CNN approach that integrated spatial and frequency features with attention mechanisms to enhance image manipulation detection, whereas MTDnet [26] and SpindleNet [27] incorporated auxiliary tasks to boost the accuracy of person re-identification. When data variations are subtle, single-task learning, as used by domain-guided dropout [28], is preferred to optimize the neuron impact on datasets. However, this can lead to undertraining of neurons shared across tasks. [PERSON] et al. [29] developed the CenterTrack tracker for online, real-time object tracking by associating objects between adjacent frames.