团队2018级研究生朱凯赢同学的论文“Photometric transfer for direct visual odometry”被SCI期刊《Knowledge-Based Systems》 录用，祝贺！

Abstract:
Due to efficient photometric information utilization, direct visual odometry (DVO) is getting widely used to estimate the ego-motion of moving cameras as well as map the environment from videos simultaneously, especially in challenging weak-texture scenarios. However, DVO suffers from brightness discrepancies since it directly utilizes intensity patterns of pixels to register frames for camera pose estimation. Most existing brightness transfer methods build a fixed transfer function which is inappropriate for successive and inconsistent brightness changes in practice. To overcome this problem, we propose a Photometric Transfer Net (PTNet) which is trained to pixel-wisely remove brightness discrepancies between two frames without ruining the context information. Photometric consistency in DVO is obtained by adjusting the source frame according to the reference frame. Since no dataset is available for training the photometric transfer model, we augment the EuRoC dataset by generating a certain number of frames with different brightness levels for each original frame by a nonlinear transformation. Afterwards, required training data containing various brightness changes and scene movements along with ground truth can be collected from the extended sequences. Evaluations on both real-world and synthetic datasets demonstrate the effectiveness of the proposed model. Assessment on an unseen dataset with fixed model parameters trained on another dataset proves the generalization ability of the model. Furthermore, we embed the model into DVO to preprocess input data with brightness discrepancies. Experimental results show that PTNet-based DVO achieves more robust initialization and accurate pose estimation than the original one.

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Keywords: Photometric transfer, Direct visual odometry, Data augmentation, Brightness discrepancy, Deep learning.

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团队2017级研究生黎阳同学的论文“Effective Person Re-identification by Self-Attention Model Guided Feature Learning”被SCI期刊《Knowledge-Based Systems》 录用，祝贺！

Abstract:
Person re-identification (re-ID), of which the goal is to recognize person identities of images captured by non-overlapping cameras, is a challenging topic in computer vision. Most existing person re-ID methods conduct directly on detected objects, which ignore the space misalignment caused by detectors, human pose variation, and occlusion problems. To tackle the above mentioned difficulties, we propose a self-attention model guided deep convolutional neural network(DCNN) to learn robust features from image shots. Kernels of the self-attention model evaluate weights for the importance of different person regions. To solve the local feature dependence problem of feature extraction, the non-local feature map generated by the self-attention model is fused with the original feature map generated from the resnet-50. Furthermore, the loss function considers both the cross-entropy loss and the triplet loss in the training process, which enables the network to capture common characteristics within the same individuals and significant differences between distinct persons. Extensive experiments and comparative evaluations show that our proposed strategy outperforms most of the state-of-the-art methods on standard datasets: Market-1501, DukeMTMC-reID, and CUHK03.

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Keywords: Personre-identification, Feature extraction, Self-attention, Cross-entropy loss, Triplet loss.

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2019年9月21日，应AEIC组委会邀请，团队负责人姜晓燕博士前往大连参加International Conference on Frontiers Technology of Information and Computer会议，并做主旨报告。

团队负责人姜晓燕老师的论文“Unsupervised learning of depth and ego-motion with spatial-temporal geometric constraints”在IEEE International Conference on Multimedia and Expo (ICME) 中被接收，祝贺！

Abstract:
In this paper, we propose an unsupervised joint deep learning pipeline for depth and ego-motion estimation that explicitly incorporated with traditional spatial-temporal geometric constraints. The stereo reconstruction error provides the spatial geometric constraint to estimate the absolute scale depth. Meanwhile, the depth map with absolute scale and a pre-trained pose network serve as a good starting point for direct visual odometry (DVO), resulting in a fine-grained ego-motion estimation with the additional back-propagation signals provided to the depth estimation network. The proposed joint training pipeline enables an iterative coupling optimization process for accurate depth and precise ego-motion estimation. The experimental results show the state-of-the-art performance for monocular depth and ego-motion estimation on the KITTI dataset and a great generalization ability of the proposed approach.

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Keywords: Depth, ego-motion, geometric constraint, visual odometry

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