547 research outputs found
Video Salient Object Detection via Fully Convolutional Networks
This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the densely annotated video segmentation data set (MAE of .06) and the Freiburg-Berkeley Motion Segmentation data set (MAE of .07), and do so with much improved speed (2 fps with all steps)
Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement
We present a novel spatiotemporal saliency detection method to estimate salient regions in videos based on the gradient flow field and energy optimization. The proposed gradient flow field incorporates two distinctive features: 1) intra-frame boundary information and 2) inter-frame motion information together for indicating the salient regions. Based on the effective utilization of both intra-frame and inter-frame information in the gradient flow field, our algorithm is robust enough to estimate the object and background in complex scenes with various motion patterns and appearances. Then, we introduce local as well as global contrast saliency measures using the foreground and background information estimated from the gradient flow field. These enhanced contrast saliency cues uniformly highlight an entire object. We further propose a new energy function to encourage the spatiotemporal consistency of the output saliency maps, which is seldom explored in previous video saliency methods. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods
Modelling of TemporalāSpatial Distribution of Airplane Wake Vortex for Scattering Analysis
Aircraft wake vortex is a pair of intensive counterārotating airflow generated by a flying aircraft. Wake vortex is one of the most dangerous hazards in aviation because it may cause a following aircraft to roll out of control, particularly during the taking off and landing phases. The realātime detection of wake vortex is a frontier scientific problem emerging from many fields like aviation safety and atmospheric physics, and the dynamics and scattering characteristics of it remain as key problems to develop corresponding detection technologies. This chapter aims at presenting a simulation scheme for the dynamics of wake vortex under different weather conditions. For wake vortex generated in clear air, changes of the atmospheric dielectric constant produced by the density variation and water vapour variation are analysed; for wake vortex generated in rainy condition, the raindrop distribution in the wake vortex is also analysed. Both of them are essential for further analysing the scattering characteristics and developing new detection algorithms
Semantic-aware Consistency Network for Cloth-changing Person Re-Identification
Cloth-changing Person Re-Identification (CC-ReID) is a challenging task that
aims to retrieve the target person across multiple surveillance cameras when
clothing changes might happen. Despite recent progress in CC-ReID, existing
approaches are still hindered by the interference of clothing variations since
they lack effective constraints to keep the model consistently focused on
clothing-irrelevant regions. To address this issue, we present a Semantic-aware
Consistency Network (SCNet) to learn identity-related semantic features by
proposing effective consistency constraints. Specifically, we generate the
black-clothing image by erasing pixels in the clothing area, which explicitly
mitigates the interference from clothing variations. In addition, to fully
exploit the fine-grained identity information, a head-enhanced attention module
is introduced, which learns soft attention maps by utilizing the proposed
part-based matching loss to highlight head information. We further design a
semantic consistency loss to facilitate the learning of high-level
identity-related semantic features, forcing the model to focus on semantically
consistent cloth-irrelevant regions. By using the consistency constraint, our
model does not require any extra auxiliary segmentation module to generate the
black-clothing image or locate the head region during the inference stage.
Extensive experiments on four cloth-changing person Re-ID datasets (LTCC, PRCC,
Vc-Clothes, and DeepChange) demonstrate that our proposed SCNet makes
significant improvements over prior state-of-the-art approaches. Our code is
available at: https://github.com/Gpn-star/SCNet.Comment: Accepted by ACM MM 202
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