"*Sharing local information in scanning-window detection"@en . "21230" . "[F20CA8D732F2]" . "3"^^ . "Praha" . . . . "Pokorn\u00FD, Jan" . . "11"^^ . . "*Object detection is a classic task in computer vision. WaldBoost algorithm is a state-of-the-art method for object detection due its high detection accuracy and real-time speed. However, since the traditional scanning window method classifies all the windows independently and doesn't make use of the information shared among overlapping windows,there is still a possibility of a significant speed-up by exploiting this property. We evaluate number of scanning patterns and predictors for spatially adjacent windows, inspired by work of Hradi\u0161 et. al. Furthermore, we generalize this idea from spatially adjacent widows to multiple scales and propose {WaldBoost with Crosstalk Prediction}. Evaluating on a state-of-the-art dataset for face detection, we show that a significant speed-up can be achieved with {WaldBoost with Crosstalk Prediction} with no or a little loss of precision, outperforming the reference method of Hradi\u0161 et. al."@en . "RIV/68407700:21230/14:00223835" . . "44713" . "*Sharing local information in scanning-window detection" . "RIV/68407700:21230/14:00223835!RIV15-MSM-21230___" . "N" . . . . "*Sharing local information in scanning-window detection"@en . "Matas, Ji\u0159\u00ED" . . . . "Object detection; Sequential decison; WaldBoost; Scanning window"@en . "Toyota Motor Europe NV/SA TMEM" . . . "*Object detection is a classic task in computer vision. WaldBoost algorithm is a state-of-the-art method for object detection due its high detection accuracy and real-time speed. However, since the traditional scanning window method classifies all the windows independently and doesn't make use of the information shared among overlapping windows,there is still a possibility of a significant speed-up by exploiting this property. We evaluate number of scanning patterns and predictors for spatially adjacent windows, inspired by work of Hradi\u0161 et. al. Furthermore, we generalize this idea from spatially adjacent widows to multiple scales and propose {WaldBoost with Crosstalk Prediction}. Evaluating on a state-of-the-art dataset for face detection, we show that a significant speed-up can be achieved with {WaldBoost with Crosstalk Prediction} with no or a little loss of precision, outperforming the reference method of Hradi\u0161 et. al." . "Trefn\u00FD, Ji\u0159\u00ED" . "*Sharing local information in scanning-window detection" . . . . "3"^^ .