# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
###### Abstract  
Multi-object tracking aims to track multiple objects across consecutive frames in a video, assigning a unique classifier to each object. However, issues such as occlusions, directional changes, or shape alterations can cause appearance variations, leading to detection and matching problems that in turn result in frequent ID switches. To solve these issues, this paper proposes a two-stage multi-object tracking framework based on a spatial and temporal fusion algorithm. First, the video frames are processed by a detector to identify objects and form rectangular detection areas. Meanwhile, an estimator predicts the target rectangular areas in the next frame. Then, we extract the optical flow of the target pixels within the detection and prediction areas, and then a temporal information model is established by calculating the average of the target pixels' optical flow. Afterward, we present a spatial information model using the R-IoU (Reverse of Intersection over Union) between the detection and prediction areas. This spatial and temporal information is combined with weighted matrix fusion, which achieves the feature matching and association task. Finally, we implement a two-stage association multi-object tracking model using the mentioned fusion algorithm. Experiments on the MOTChallenge dataset using the official detector show that our two-stage multi-object tracking method based on the spatial and temporal fusion algorithm is robust in handling occlusions and ID switch issues. As of the submission of this paper, the proposed method has achieved the top ranking in the MOT17 benchmark when evaluated with the official detector. Aticle  
17 November 202421 December 20242 December 20  
positives. The system incorrectly detects false targets. In such cases, the system may erroneously associate real targets with false ones, or it may associate multiple real targets with the same false target, resulting in tracking failure. (4) Target association problem. Due to insufficient utilization of various features of the target by the tracker, the system has difficulty maintaining continuous association and matching of the same target during the association phase, leading to identity confusion or tracking loss. These issues make it difficult for most tracking algorithms to achieve high tracking performance. Many researchers currently address the challenges in multi-object tracking tasks within the framework of detection-based tracking. [PERSON] employed the BASE [1] minimalist probabilistic visual tracking method, which achieved significant results in enhancing tracking performance. However, in complex scenarios, such as handling highly irregular movements in the DanceTrack dataset, BASE's motion model assumption of continuous and slow-changing motion may lead to a decline in tracking performance. [PERSON] proposed the SparseTrack [2] multi-object tracking method, which addresses occlusion issues through pseudo-depth-based scene decomposition and has achieved certain successes in improving tracking performance. However, in scenes with fast-moving and deformable targets, the pseudo-depth method struggles to accurately capture the relative depth relationships of targets, affecting tracking performance. [PERSON] introduced the ByteTrack [3] multi-object tracking algorithm framework, which solves tracking issues caused by target occlusion and the discarding of detection boxes with low confidence by using a high-performance detector. However, since it only utilizes one feature, either IoU or ReID, the method's insufficient use of target features negatively impacts tracking performance. [PERSON] proposed the BoTSORT [4] multi-object tracking method, which significantly improves tracking performance by enhancing the Kalman filter in ByteTrack, introducing camera motion compensation, and optimizing the IoU-ReID fusion strategy.