OneTrack-M: A Multitask Approach for Transformer-Based MOT Models

Multi-Object Tracking (MOT) is a critical problem in computer vision, essential for understanding how objects move and interact in videos. This field faces significant challenges such as occlusions and complex environmental dynamics, impacting model accuracy and efficiency. While traditional approaches have relied on Convolutional Neural Networks (CNNs), the introduction of transformers has brought substantial advancements. This work introduces OneTrack-M, a transformer-based MOT model that enhances tracking computational efficiency and accuracy. Our approach introduces the transformer encoder as the model backbone, significantly reducing processing time and increasing inference speed. Additionally, we employ innovative data preprocessing and multitask training techniques to address occlusion and diverse objective challenges within a single set of weights. Experimental results demonstrate that OneTrack-M achieves at least 25% faster inference times compared to state-of-the-art models in the literature while maintaining or improving tracking accuracy metrics. These improvements highlight the potential of the proposed solution for real-time applications such as autonomous vehicles, surveillance systems, and robotics, where rapid responses are crucial for system effectiveness.