# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments  
This section outlines the design and analysis of the experiments conducted. The experiments were performed in two parts. The first part involved the evaluation of the feature models. We adjusted the network architecture and training strategies to assess their performance to identify the optimal model for the subsequent multi-object trackingexperiments. The second part focused on multi-object tracking tests, in which the best feature model was applied to multiple test videos, primarily tracking pedestrians. We tested different tracker initialization methods and adjusted various parameters to determine the most effective approach. Finally, we compared our results with several state-of-the-art tracking methods using popular multi-object tracking benchmark videos. Section 3.1 outlines the evaluation criteria of this research model. Section 3.2 evaluates the performance of single-task joint learning and cosine metric learning. Section 3.3 compares our proposed method with state-of-the-art algorithms using a multi-object tracking dataset and provides a detailed analysis and discussion of the experimental results. The experimental setup consisted of an Intel Core i7-930 CPU, Nvidia GeForce GTX 1080 Ti GPU, and 24 GB RAM, running Ubuntu 14.04 LTS with CUDA 8.0 and CUDNN v6 for GPU acceleration, and implemented using Python 3.6 and TensorFlow 1.4. To construct a comprehensive comparative analysis, we employed a diverse array of state-of-the-art MOT algorithms, each chosen for its distinct methodological focus within MOT, addressing critical aspects such as trajectory stability, occlusion handling, and computational efficiency. EAMTT [36] provides adaptive trajectory tracking, ensuring stability across extended sequences. CDT [37] utilizes discriminative feature learning to maintain object identity through gradual appearance variations, while MDP_SubCNN [38] applies part-based, multi-domain feature extraction to enhance resilience under occlusion. TSML_CDE [39] implements two-stream metric learning to achieve consistent object embedding, minimizing ID switches in high-mobility contexts. Deep-SORT [12] and KCF [40] serve as robust solutions for real-time processing; Deep-SORT leverages deep feature embedding for efficient object association, while KCF introduces a lightweight correlation filter-based approach suitable for resource-constrained environments. CDA-DDAL [10] and AP_HWDPL [15] are specifically optimized for occlusion robustness and hierarchical association prediction, offering reliable performance in densely populated, dynamic tracking conditions. Collectively, these algorithms provided a well-rounded framework for assessing our model's adaptability and performance across diverse, real-world tracking scenarios, emphasizing its versatility and competitive standing within the MOT landscape. Our system used a ResNet for feature extraction, with a computational complexity of \(O\big{(}n^{2}\big{)}\) per convolutional layer, depending on the kernel size and input dimensions. For Metric Learning, we employed cosine softmax, with complexity \(O(d)\), where d is the feature vector dimensionality. Tracking state prediction was achieved through a Kalman filter, operating with a complexity of \(O\big{(}m^{2}\big{)}\), where m denotes the state dimension. Object association utilized cosine distance with complexity \(O(d)\), while detection confidence assessment added minimal complexity through basic comparison operations. Combining these components, the total computational complexity of our system was dominated by the feature extraction stage (ResNet), resulting in an approximate upper bound of \(O\big{(}n^{2}\big{)}\). Given our implementation on an Intel Core i7-930 CPU (Intel Corporation, Santa Clara, CA, USA) with an Nvidia GTX 1080 Ti GPU (Nvidia Corporation, Santa Clara, CA, USA), the system achieved near real-time processing speeds.