Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D) without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.). Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP) dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.

In the development of a new product, the design team must describe the expected effects of the final products to potential users and stakeholders. However, existing prototyping tools can only present a product imperfectly, due to limitations at different levels. Specifically, the physical product model, which may be the product of 3D printing, could lack a visual interface; the presentation of the product through modeling software such as Rhinoceros 3D does not provide good realistic tactile perception; or the interface platforms, such as Axure RP, used to display the interactive effects differ from those to be used in the actual operation. Thus, we present the VPModel, a high-fidelity prototyping tool, able to integrate multiple prototyping methods simultaneously. It combines a touchable 3D-printed product model (3DPM) and a corresponding visualized virtual model, and the interactive interfaces are rendered synchronously in a mixed-reality device. Through the tangible, visual, and interactive demonstration, designers and normal users can each obtain a similar experience to the experience of the finished product. Furthermore, the VPModel also enhances design practices by enabling comparisons between modular models. However, the implementation of this system is a challenging task, which subsumes several fundamental problems as sub-tasks: object detection, real-time matching, hand-gesture detection and action recognition. To achieve the expected goals of the VPModel, this system uses physical hardware (a Microsoft MR HoloLens headset, a Leap Motion Controller, and a 3D printer) and existing machine learning algorithms. To evaluate our VPModel, we report the user experience of 16 participants, evaluated using a closed-ended questionnaire survey, a quantitative analysis of task performance, and a qualitative analysis of open-ended interviews. The results show a significant improvement in realism and enjoyment using the VPModel over the two traditional camera prototype approaches. In summary, the VPModel can be used to support design strategy and to convey design concepts fully and efficiently, which indicates a potential use for the VPModel in shortening product development cycles and reducing communication costs.

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

Using video sequences to restore 3D human poses is of great significance in the field of motion capture. This paper proposes a novel approach to estimate 3D human action via end-to-end learning of deep convolutional neural network to calculate the parameters of the parameterized skinned multi-person linear model. The method is divided into two main stages: (1) 3D human pose estimation based on a single frame image. We use 2D/3D skeleton point constraints, human height constraints, and generative adversarial network constraints to obtain a more accurate human-body model. The model is pre-trained using open-source human pose datasets; (2) Human-body pose generation based on video streams. Combined with the correlation of video sequences, a 3D human pose recovery method based on video streams is proposed, which uses the correlation between videos to generate a smoother 3D pose. In addition, we compared the proposed 3D human pose recovery method with the commercial motion capture platform to prove the effectiveness of the proposed method. To make a contrast, we first built a motion capture platform through two Kinect (V2) devices and iPi Soft series software to obtain depth-camera video sequences and monocular-camera video sequences respectively. Then we defined several different tasks, including the speed of the movements, the position of the subject, the orientation of the subject, and the complexity of the movements. Experimental results show that our low-cost method based on RGB video data can achieve similar results to commercial motion capture platform with RGB-D video data.