# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
###### Abstract  
Multi-object tracking (MOT) aims to detect objects in video sequences and associate them across frames. Currently, the mainstream research direction regarding MOT is the tracking-by-detection (TBD) framework. Tracking results are highly sensitive to detection outputs, and challenges from object occlusion and complex motion present significant obstacles in the field of MOT. To reduce dependence on detection outputs, we propose a method that integrates predictive information to improve Non-Maximum Suppression (NMS). By applying secondary modulation to the suppression scores and dynamically adjusting the suppression threshold using tracking information, our method better retains candidate boxes for occluded objects. Furthermore, to track occluding and overlapping objects more effectively, we introduce an adaptive measurement noise method that adjusts the measurement noise to mitigate the impact of object occlusion or overlap on tracking accuracy. Additionally, we enhance the affinity matrix in the association algorithm by incorporating height information, thereby improving the stability of complex moving objects. Our method outperforms the baseline model ByteTrack on the DanceTrack dataset, increasing Higher Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and the ID F1 Score (IDF1) by 10.2%, 3.0%, and 4.8%, respectively. multi-object tracking; tracking-by-detection; occlusion; complex motion +
Footnote †: journal: Applied sciences  
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and the tracking stage can achieve object association through the motion model. However, there are certain limitations to TBD-based MOT. Since the method relies on mature object detection technologies, although it leads to significant improvements in the early stages of technological development, the highly specialized detection part may negatively impact the final tracking results as the technology matures. This is because the goal of object detection is to detect all objects in a single image, while object tracking builds object trajectories across a sequence of images. As a result, researchers have begun exploring the incorporation of additional information to further improve the effectiveness of TBD-based MOT. The workflow of TBD-based MOT methods begins with the detection phase, where images are processed to detect objects and output detection boxes. These detection boxes are then passed to the tracking phase, where the system associates and matches the prediction boxes (generated from historical data) with the current detection boxes, ultimately generating object trajectories. To improve tracking performance, researchers have attempted to enhance the output of the detection phase by introducing additional information. For instance, TraDes [14] integrates tracking information into the current frame to resolve occlusion issues in detection. MOTDT [15] generates candidate boxes by predicting the object trajectory in the current frame, supplementing detection candidate boxes and thereby optimizing the detection phase's output. However, as TBD-based MOT methods rely on mature object detection technologies, these improvements have limited effectiveness in further enhancing results. At the same time, researchers have recognized that there is still considerable room for improvement in the tracking phase and have begun to optimize the prediction and association algorithms by introducing additional information. For example, DeepSORT [5] introduces deep visual features [16] and recalculates the affinity matrix using the Mahalanobis distance, significantly improving the accuracy of data association. GHOST [17] extracts object appearance features via an independent ReID module [18; 19; 20], enabling precise object association.