Advanced Intersection over Union Loss for Visual Tracking

Intersection over Union (IoU) is the most important metric in visual tracking benchmark. However, IoU cannot always accurately describe the similarity between two bounding boxes. In some cases, IoU cannot reflect the similarity of location, shape (aspect ratio) and area between two bounding boxes correctly, which means even if two group bounding boxes have same IoU, their positions, shapes and areas deviation may be different. In this paper, we propose a new evaluation metric, called Advanced Intersection over Union (AIoU), to solve this problem by adding penalties for positions, shapes and areas changes between two bounding boxes, and apply AIoU as a loss function to the bounding box regression part of Siamese tracker. By training the regression branch of Siamese tracker with AIoU loss, IoU loss and traditional minimum Mean Square Error (MSE) loss function, we show that the new AIoU loss is more effective for locating than MSE loss and IoU loss on VOT benchmark. At the same time, we combine SiamRPN with AIoU loss to form the SiamAIoU tracker and compare our method with many state-of-the-art trackers, the results show that SiamAIoU get higher scores on OTB100, VOT2016 and VOT2018. In addition, our tracker runs at 35 FPS in real time.