In foreground segmentation, it is challenging to construct an effective background model to learn the spatial-temporal representation of the background. Recently, deep learning-based background models (DBMs) with the capability of extracting high-level features have shown remarkable performance. However, the existing state-of-the-art DBMs deal with video segmentation as single-image segmentation and ignore temporal cues in video sequences. To exploit temporal data sufficiently, this paper proposes a multi-input multi-output (MIMO) DBM framework for the first time, which is partially inspired by the binocular summation effect in human eyes. Our framework is an X-shaped network which allows the DBM to track temporal changes in a video sequence. Moreover, each output branch of our model could receive visual signals from two similar input frames simultaneously like the binocular summation mechanism. In addition, our model can be trained end-to-end using only a few training examples without any postprocessing. We evaluate our method on the largest dataset for change detection (CDnet 2014) and achieve the state-of-the-art performance by an average overall F-Measure of 0.9920. INDEX TERMS Foreground segmentation, background subtraction, deep learning, focal loss, binocular summation.
Although person re-identification (ReID) has drawn increasing research attention due to its potential to address the problem of analysis and processing of massive monitoring data, it is very challenging to learn discriminative information when the people in the images are occluded, in large pose variations or from different perspectives. To address this problem, we propose a novel joint attention person ReID (JA-ReID) architecture. The idea is to learn two complementary feature representations by combining a soft pixel-level attention mechanism and a hard region-level attention mechanism. The soft pixel-level attention mechanism learns a discriminative embedding for the fine-grained information by exploring the salient parts in the feature maps. The hard region-level attention mechanism conducts uniform partitions on the convolutional feature maps for learning local features. We have achieved competitive results in three popular benchmarks, including Market1501, DukeMTMC-reID, and CUHK03. The experimental results verify the adaptability of the joint attention mechanism to non-rigid deformation of the human body, which can effectively improve the accuracy of ReID.
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