Recently, large-scale synthetic datasets are shown to be very useful for generalizable person re-identification. However, synthesized persons in existing datasets are mostly cartoon-like and in random dress collocation, which limits their performance. To address this, in this work, an automatic approach is proposed to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart. Specifically, based on UV texture mapping, two cloning methods are designed, namely registered clothes mapping and homogeneous cloth expansion. Given clothes keypoints detected on person images and labeled on regular UV maps with clear clothes structures, registered mapping applies perspective homography to warp real-world clothes to the counterparts on the UV map. As for invisible clothes parts and irregular UV maps, homogeneous expansion segments a homogeneous area on clothes as a realistic cloth pattern or cell, and expand the cell to fill the UV map. Furthermore, a similarity-diversity expansion strategy is proposed, by clustering person images, sampling images per cluster, and cloning outfits for 3D character generation. This way, virtual persons can be scaled up densely in visual similarity to challenge model learning, and diversely in population to enrich sample distribution. Finally, by rendering the cloned characters in Unity3D scenes, a more realistic virtual dataset called ClonedPerson is created, with 5,621 identities and 887,766 images. Experimental results show that the model trained on ClonedPerson has a better generalization performance, superior to that trained on other popular real-world and synthetic person re-identification datasets. The ClonedPerson project is available at https://github.com/Yanan-Wang-cs/ClonedPerson.

Recently, convolutional neural networks (CNNs) have been applied in background subtraction (change detection) and gained notable improvements. Two typical methods have been proposed. The first one learns a specific CNN model for each video, but requires manual labeling of training frames on the fly. The other one learns a universal model offline, however, limits its performance in handling various surveillance scenarios. To address these problems, in this paper, a new deep background subtraction method is proposed by introducing a guided learning strategy. The main idea is to learn a specific CNN model for each video to ensure accuracy, but manage to avoid manual labeling. To achieve this, firstly we apply the SubSENSE algorithm [1] to get an initial segmentation, and then an adaptive strategy is designed to select reliable pixels to guide the CNN training. Besides, we also design a simple strategy to automatically select informative frames for guided learning. Experiments on the largest background subtraction benchmark CDnet2014 show that the proposed guided deep learning method outperforms existing state of the arts.

Vehicle detection is still a challenge in complex traffic scenes, especially for vehicles of tiny scales. Though RCNN based two-stage detectors have demonstrated considerably good performance, less attention has been paid to the quality of the first stage, where, however, tiny vehicles are very likely to be missed. In this paper, we propose a deep network for accurate vehicle detection, with the main idea of using a relatively large feature map for proposal generation, and keeping ROI feature's spatial layout to represent and detect tiny vehicles. However, large feature maps in lower levels of a deep network generally contain limited discriminant information. To address this, we introduce a backward feature enhancement operation, which absorbs higher level information step by step to enhance the base feature map. By doing so, even with only 100 proposals, the resulting proposal network achieves an encouraging recall over 99%. Furthermore, unlike a common practice which flatten features after ROI pooling, we argue that for a better detection of tiny vehicles, the spatial layout of the ROI features should be preserved and fully integrated. Accordingly, we use a multi-path light-weight processing chain to effectively integrate ROI features, while preserving the spatial layouts. Experiments done on the challenging DETRAC vehicle detection benchmark show that the proposed method largely improves a competitive baseline (ResNet50 based Faster RCNN) by 16.5% mAP, and it outperforms all previously published and unpublished results.