Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos

• URL: http://arxiv.org/abs/2104.08241v1
• Date: Thu, 15 Apr 2021 14:32:12 GMT
• Title: Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos
• Authors: Jiawei Liu, Zheng-Jun Zha, Wei Wu, Kecheng Zheng, Qibin Sun
• Abstract summary: We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
• Score: 78.45050529204701
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re-identification is to effectively exploit both spatial and temporal clues from video sequences. In this work, we propose a novel Spatial-Temporal Correlation and Topology Learning framework (CTL) to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. Specifically, CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body at multiple granularities as graph nodes. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body. Moreover, a 3D graph convolution and a cross-scale graph convolution are designed, which facilitate direct cross-spacetime and cross-scale information propagation for capturing hierarchical spatial-temporal dependencies and structural information. By jointly performing the two convolutions, CTL effectively mines comprehensive clues that are complementary with appearance information to enhance representational capacity. Extensive experiments on two video benchmarks have demonstrated the effectiveness of the proposed method and the state-of-the-art performance.

Related papers

• Understanding Spatio-Temporal Relations in Human-Object Interaction using Pyramid Graph Convolutional Network [2.223052975765005]
  We propose a novel Pyramid Graph Convolutional Network (PGCN) to automatically recognize human-object interaction. The system represents the 2D or 3D spatial relation of human and objects from the detection results in video data as a graph. We evaluate our model on two challenging datasets in the field of human-object interaction recognition.
  arXiv  Detail & Related papers  (2024-10-10T13:39:17Z)
• Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation [64.85974098314344]
  Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. Inherently, object pairs and their relationships enjoy spatial co-occurrence correlations within each image and temporal consistency/transition correlations across different images. We propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism.
  arXiv  Detail & Related papers  (2023-09-23T02:40:28Z)
• Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification [91.56939957189505]
  We propose a novel spatial-temporal complementary learning framework named Deeply-Coupled Convolution-Transformer (DCCT) for high-performance video-based person Re-ID. Our framework could attain better performances than most state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-27T12:16:44Z)
• Keypoint Message Passing for Video-based Person Re-Identification [106.41022426556776]
  Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Existing methods are mostly based on convolutional neural networks (CNNs), whose building blocks either process local neighbor pixels at a time, or, when 3D convolutions are used to model temporal information, suffer from the misalignment problem caused by person movement. In this paper, we propose to overcome the limitations of normal convolutions with a human-oriented graph method. Specifically, features located at person joint keypoints are extracted and connected as a spatial-temporal graph
  arXiv  Detail & Related papers  (2021-11-16T08:01:16Z)
• GTA: Global Temporal Attention for Video Action Understanding [51.476605514802806]
  We introduce Global Temporal Attention (AGT), which performs global temporal attention on top of spatial attention in a decoupled manner. Tests on 2D and 3D networks demonstrate that our approach consistently enhances temporal modeling and provides state-of-the-art performance on three video action recognition datasets.
  arXiv  Detail & Related papers  (2020-12-15T18:58:21Z)
• Comparison of Spatiotemporal Networks for Learning Video Related Tasks [0.0]
  Many methods for learning from sequences involve temporally processing 2D CNN features from the individual frames or directly utilizing 3D convolutions within high-performing 2D CNN architectures. This work constructs an MNIST-based video dataset with parameters controlling relevant facets of common video-related tasks: classification, ordering, and speed estimation. Models trained on this dataset are shown to differ in key ways depending on the task and their use of 2D convolutions, 3D convolutions, or convolutional LSTMs.
  arXiv  Detail & Related papers  (2020-09-15T19:57:50Z)
• Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos [85.6430597108455]
  We propose a novel Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person re-identification in videos. It captures the common salient foreground regions among video frames and explores the spatial-temporal long-range context interdependency from such regions. Multiple spatialtemporal interaction modules within CSTNet are proposed, which exploit the spatial and temporal long-range context interdependencies on such features and spatial-temporal information correlation.
  arXiv  Detail & Related papers  (2020-04-10T10:23:58Z)
• A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video [7.647599484103065]
  We improve the learning of constraints in human skeleton by modeling local global spatial information via attention mechanisms. Our approach effectively mitigates depth ambiguity and self-occlusion, generalizes to half upper body estimation, and achieves competitive performance on 2D-to-3D video pose estimation.
  arXiv  Detail & Related papers  (2020-03-11T14:54:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.