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This paper introduces a method to guide the visual search towards a searched object, analogously to what is performed by the top-down visual attention mechanism. This is done by prioritizing scene descriptors based on their Hamming distance to the descriptors of the target. The proposal has constant space and time complexity in relation to the number of descriptors of the searched object. Moreover,...
Despite significant progress in pedestrian detection has been made in recent years, detecting pedestrians in crowded scenes remains a challenging problem. In this paper, we propose to use visual contexts based on scale and occlusion cues from detections at proximity to better detect pedestrians for surveillance applications. Specifically, we first apply detectors based on full body and parts to generate...
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as...
This paper presents an approach for visual tracking, consisting of two combination modules, which are global detector and local image patch matching. The former gives the classification response for each object candidate specified by the sliding window in the searching region. The classification can be performed by any global detector, which is based on the feature from the local patch in the object...
This paper presents a fast algorithm for deriving the defocus map from a single image. Existing methods of defocus map estimation often include a pixel-level propagation step to spread the measured sparse defocus cues over the whole image. Since the pixel-level propagation step is time-consuming, we develop an effective method to obtain the whole-image defocus blur using oversegmentation and transductive...
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