# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
###### Abstract
FairMOT [7] adopts an anchor-free CenterNet [12] architecture and designs a dual-branch network, balancing object detection and appearance feature extraction tasks, greatly enhancing association performance. CSTrack [21] introduces a cross-correlation network to improve the output of network branches. QDTrack [22] combines similarity learning to enhance long-term ReID performance. FineTrack [9] explores different fine-grained representations to improve discriminative representation. Byte-Track [6] enhances tracking accuracy and robustness by performing secondary matching on low-threshold detection boxes, helping to locate occluded objects, reduce missed detections, and effectively filter the background to minimize the impact of False Positives on tracking results. OC-SORT [10] reduces the accumulation of prediction errors from the Kalman filter (KF) during occlusion by constructing a virtual tracklet and considers the motion direction when calculating the affinity matrix, thereby improving association accuracy. Additionally, some research focuses on improving the KF, such as the NSA-KF [8; 23], which optimizes the calculation of measurement noise and dynamically adjusts the weighting ratio between measurements and predictions during the update step to enhance filter performance. Other studies [1; 3; 24] aim to mitigate the adverse effects of camera motion on tracking performance by using Enhanced Correlation Coefficient Maximization (ECC) [25] or feature matching with ORB [26] for camera motion compensation, thereby reducing rigid displacement bias caused by camera movement. We aimed to improve both the detection and tracking stages by introducing additional information to achieve better performance. Since the input of the tracking phase relies on the output of the detection phase, tracking results are highly sensitive to detection outputs. Some studies have proposed end-to-end MOT methods to address this issue, such as CenterTrack [27], which merges the detection and tracking stages by locating objects and predicting their displacement from the previous frame, and MOTR [28], which employs a Transformer architecture [29] to model the relationships between objects using global context information, building an end-to-end MOT model. However, their accuracy has not yet reached an ideal level. During the object detection process, the detector aims to identify all objects within a single image. As a result, low-confidence detection boxes for background or occluded objects are often discarded by Non-Maximum Suppression (NMS), leading to tracking failures. In contrast, tracking operates on a video sequence composed of multiple images, allowing it to leverage historical information to implicitly distinguish between background and occluded objects. Based on this insight, we improved the TBD-based MOT method by delaying the execution of NMS to the tracking phase and integrating predictive information. This approach enables effective differentiation between a low-confidence background and occluded objects, providing higher-quality detection information for the tracking phase and improving overall tracking performance. Low-confidence objects can be beneficial for improving tracking performance when passed to the tracking phase. However, these low-confidence detection boxes often have poor quality, typically due to positioning errors caused by foreground occlusion or overlapping objects. To address these issues, we proposed an adaptive measurement noise method to enhance the motion prediction model in the tracking phase. For low-confidence detection boxes caused by foreground occlusion, the positioning errors are significant, leading to higher uncertainty. Based on this observation, after successful trajectory association, we adjusted the measurement noise covariance in the KF update step to be inversely proportional to the confidence level--meaning that the lower the confidence, the greater the measurement noise.