# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
###### Abstract
Although this method leverages appearance information to some extent, changes in pedestrian posture under different viewpoints affect the shape of detection boxes, leading to inaccurate IoU calculations and increased difficulty in extracting target appearance features. This can result in inaccurate matching during the target association process. With the advancement and optimization of object detection technologies, many researchers have opted to integrate high-performance detectors [5] with trackers to obtain high-precision detection results for optimizing tracking performance. However, this approach not only increases the cost of tracking tasks but also provides only marginal improvements. This method still generates a significant number of IDs. In recent years, some researchers have adopted neural network-based feature learning models to enhance the robustness of multi-object tracking under occlusion. However, since these methods rely solely on spatial features such as target appearance [6], the reliability of these features is low in complex environments with mutual occlusion, leading to inaccurate multi-object tracking. [PERSON] et al. [7] argue that temporal features (such as motion direction, speed, and acceleration) are also crucial tracking information that can effectively enhance multi-object tracking performance. To address the aforementioned issues, we propose the IOF-Tracker multi-object tracking method, which is a two-stage model that combines spatial (R-IoU, Reverse of Intersection over Union) and temporal (Optical Flow) fusion algorithms. This method enables the tracker to fully utilize the optical flow motion features of the targets and combine the reverse intersection over union (R-IoU) between the detection and prediction regions for template matching. This reduces the frequent ID switching during tracking and allows the tracker to achieve long-term continuous tracking. The method enhances the accuracy and robustness of the tracker in handling multi-object tracking tasks, leading to more stable and precise multi-object tracking. Both the optical flow method [8; 9] and the IoU method [10] are classical techniques. In this paper, we have chosen these two classic features as the base lines, and the R-IoU and mean optical flow are designed to make the features more suitable for multi-object tracking. Although some existing multi-object tracking methods [11] in the literature also involve the IoU and optical flow features, their performance still needs to be improved. In contrast, our method leverages the mean optical flow of targets to handle more complex scenes and combines R-IoU features to achieve multi-feature association, resulting in better tracking performance. During the tracking process, we first use an existing detector to identify target regions in each frame. We then apply a Kalman filter to predict the positions of each target in future frames. The Kalman filter predicts the current position based on the target's motion model and the previous state. It corrects this prediction using the detected position information. By calculating the Kalman gain, which balances the weight between prediction and observation, the target state is updated to more accurately estimate the possible positions of the target in future frames, i.e., the predicted regions. Next, we extract optical flow information from both the detection regions and predicted regions of each target. During extraction, we average the optical flow information of all pixels within the region to obtain an average optical flow vector, reducing the noise interference in the optical flow. Subsequently, we calculate the R-IoU between the detection regions and predicted regions to obtain spatial information, which measures their spatial overlap. The feature fusion is achieved by weighting the addition of these two pieces of information in matrix dimensions. Finally, we integrate this fusion model into each stage of the two-stage cascaded matching.