Zhu, HaoPorikli, Fatih2021-05-121057-7149http://hdl.handle.net/1885/232668Tracking objects across multiple frames is a well-investigated problem in computer vision. The majority of the existing algorithms that assume an accurate initialization is readily available. However, in many real-life settings, in particular for applications where the video is streaming in real time, the initialization has to be provided by a human operator. This limitation raises an inevitable uncertainty issue. Here, we first collect a large and new data set of inputs that consists of more than 20 K human initialization clicks , by several subjects under three practical user interface scenarios for the popular TB50 tracking benchmark. We analyze the factors and mechanisms of human input, derive statistical models, and show that human input always contains deviations, which exacerbate further when the relative object-camera motion becomes large. We also design and evaluate alternative refinement schemes, and propose a strategy that refits an object window on the most probable target region after a single click. To compensate for the human initialization errors, our method generates window proposals using objectness cues extracted from color and motion attributes, accumulates them into a likelihood map that is weighted by the initial click position and visual saliency scores, and assigns the final window by the maximum likelihood estimate. Our experiments demonstrate that the presented refinement strategy effectively reduces human input errors.The work of H. Zhu was supported in part by the National Natural Science Foundation of China under Grant 61321002 and Grant 61120106010, and in part by the China Scholarship Council under Grant 201406030023. The work of F. Porikli was supported by the Australian Research Councils Discovery Projects under Project DP150104645application/pdfen-AU© 2016 IEEEObject initializationobject trackinghuman-computer interactiveerror compensationAutomatic refinement strategies for manual initialization of object trackers201710.1109/TIP.2016.26338742020-11-23