Detection Windows Fusion Based on Theory of Heat Diffusion

Detection windows fusion is an important step of object detection based on sliding window. To overcome shortcomings of traditional detection fusion methods, this paper proposes a novel one. The method treats every preliminary window as a location in system, and heat conductivity between two locations is calculated by detection scores and overlapping area of corresponding windows. Finally, the detection windows fusion task is modeled by temperature maximization on linear anisotropic heat diffusion, of which the temperature maximization with finite K heat sources corresponds to K final windows. This paper obtains a near-optimal solution of objective function by a greedy algorithm. Experimental results on VOC2009 and INRIA pedestrian datasets show that our method not only deletes overlapping detections, but also rejects false positives and prevents interference between adjacent objects. Compared with traditional non maximum suppression, our method can obtain higher detection precision without loss of recall rates.